Stylistic Features of The Rainbow by D.H.Lawrence——A Stylistic Analysis on an Excerpt from the Novel

It should be presupposed that all writers and texts have their individual qualities before the analysis goes ahead.In this paper,the analysis will be made respectively in terms of the four categories listed as follows:lexical categories,grammatical categories,figures of speech,and cohesion and context.We can draw a conclusion that the style of D.H.Lawrence is simpleness and directness and he seems not fond of complicating the superficial account,and turns to the natural and the essential tools to reveal the most original and animalized nature of the human being which,in his mind,is the most important and the essence of the nature rules.

Research on Privacy Disclosure Detection Method in Social Networks Based on Multi-Dimensional Deep Learning

In order to effectively detect the privacy that may be leaked through social networks and avoid unnecessary harm to users,this paper takes microblog as the research object to study the detection of privacy disclosure in social networks.First,we perform fast privacy leak detection on the currently published text based on the fastText model.In the case that the text to be published contains certain private information,we fully consider the aggregation effect of the private information leaked by different channels,and establish a convolution neural network model based on multi-dimensional features(MF-CNN)to detect privacy disclosure comprehensively and accurately.The experimental results show that the proposed method has a higher accuracy of privacy disclosure detection and can meet the real-time requirements of detection.

Multi-dimensional and Multi-threshold Airframe Damage Region Division Method Based on Correlation Optimization

In order to obtain the image of airframe damage region and provide the input data for aircraft intelligent maintenance,a multi-dimensional and multi-threshold airframe damage region division method based on correlation optimization is proposed.On the basis of airframe damage feature analysis,the multi-dimensional feature entropy is defined to realize the full fusion of multiple feature information of the image,and the division method is extended to multi-threshold to refine the damage division and reduce the impact of the damage adjacent region’s morphological changes on the division.Through the correlation parameter optimization algorithm,the problem of low efficiency of multi-dimensional multi-threshold division method is solved.Finally,the proposed method is compared and verified by instances of airframe damage image.The results show that compared with the traditional threshold division method,the damage region divided by the proposed method is complete and accurate,and the boundary is clear and coherent,which can effectively reduce the interference of many factors such as uneven luminance,chromaticity deviation,dirt attachment,image compression,and so on.The correlation optimization algorithm has high efficiency and stable convergence,and can meet the requirements of aircraft intelligent maintenance.

基于特征加权的Category ART网络及应用

特征加权是特征选择的一般情况,它能更加细致地区分特征对结果影响的程度,往往能够获得比特征选择更好的或者至少相等的性能。该文采用自适应遗传算法来优化Category ART网络的特征权值,提出了一种改进的Category ART网络FWART。在UCI标准数据集上的实验表明,FWART网络获得了比Category ART网络更好的泛化能力。将该网络应用在地震震型预报上,取得了很好的预报效果。

Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor

Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it.Therefore,a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments.Firstly,the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network(CNN)to form a complete and stable multi-dimensional feature set.Secondly,to obtain a weighted multi-dimensional feature set,the multi-dimensional feature sets of similar sensors are combined,and the entropy weight method is used to weight these features to reduce the interference of insensitive features.Finally,the attention mechanism is introduced to improve the dual-channel CNN,which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors,to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis.Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy.It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods.This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.

Enhanced Answer Selection in CQA Using Multi-Dimensional Features Combination

Community Question Answering(CQA) in web forums, as a classic forum for user communication,provides a large number of high-quality useful answers in comparison with traditional question answering.Development of methods to get good, honest answers according to user questions is a challenging task in natural language processing. Many answers are not associated with the actual problem or shift the subjects,and this usually occurs in relatively long answers. In this paper, we enhance answer selection in CQA using multidimensional feature combination and similarity order. We make full use of the information in answers to questions to determine the similarity between questions and answers, and use the text-based description of the answer to determine whether it is a reasonable one. Our work includes two subtasks:(a) classifying answers as good, bad, or potentially associated with a question, and(b) answering YES/NO based on a list of all answers to a question. The experimental results show that our approach is significantly more efficient than the baseline model, and its overall ranking is relatively high in comparison with that of other models.

Event-based Two-stage Non-intrusive Load Monitoring Method Involving Multi-dimensional Features

This paper proposes an event-based two-stage Nonintrusive load monitoring(NILM)method involving multidimensional features,which is an essential technology for energy savings and management.First,capture appliance events using a goodness of fit test and then pair the on-off events.Then the multi-dimensional features are extracted to establish a feature library.In the first stage identification,several groups of events for the appliance have been divided,according to three features,including phase,steady active power and power peak.In the second stage identification,a“one against the rest”support vector machine(SVM)model for each group is established to precisely identify the appliances.The proposed method is verified by using a public available dataset;the results show that the proposed method contains high generalization ability,less computation,and less training samples.

A Comparative Study on Two Techniques of Reducing the Dimension of Text Feature Space

With the development of large scale text processing, the dimension of text feature space has become larger and larger, which has added a lot of difficulties to natural language processing. How to reduce the dimension has become a practical problem in the field. Here we present two clustering methods, i.e. concept association and concept abstract, to achieve the goal. The first refers to the keyword clustering based on the co occurrence of

融合概率类别特征增强的短文本分类

对短文本所含信息量缺乏而导致分类准确度难以提升的问题进行研究,提出一种融合概率类别特征增强的短文本分类网络模型FT_BDCNN。将N-gram处理后产生的N元词典通过TF-IDF分离出具有概率类别区分度的特征信息(FT模块);将向量化表示后的文本信息输入到改进后的特征提取模块中;将两个模块的输出进行特征融合,完成文本分类。实验结果表明,所提模型在THUCNews数据集上的F1值达到91.91%。FT模块可以与现有分类模型进行融合,提升模型的分类性能。

基于加强特征提取的道路病害检测算法

针对道路病害区域小、类别数量不均衡导致检测困难的问题,提出基于YOLOv7-tiny的道路病害检测算法RDD-YOLO。首先,采用K-means++算法得到拟合目标尺寸更好的锚框。其次,在小目标检测支路上使用量化感知重参数化模块(QARepVGG),增强浅层特征提取,同时构建加强注意力模块(AM-CBAM)嵌入颈部的3个输入,抑制复杂背景干扰。然后,设计特征融合模块(Res-RFB),模拟人眼扩大感受野融合多尺度信息,提高表征能力;另外,构造轻量级解耦头(S-DeHead)提高小目标检测精确率。最后,采用归一化Wasserstein距离度量(NWD)优化小目标定位过程,并缓解样本不均衡问题。实验结果表明,与YOLOv7-tiny相比,RDD-YOLO算法在仅增加0.71×10^(6)参数量和1.7 GFLOPs计算量的成本下,mAP50提高6.19个百分点,F1-Score提高5.31个百分点,并且检测速度达到135.26 frame/s,满足道路养护工作中对检测精度和速度的需求。

Multi-dimensional Classification via Selective Feature Augmentation

In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented features.In this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented features.Specifically, by assuming that only part of the augmented features is pertinent and useful for each dimension′s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard k NN, weighted k NN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.

基于特征提取和SVM分类的LED芯片缺陷快速检测与实现

针对LED芯片工业化生产中人工目检缺陷检测效率低,速度慢,易疲劳,受主观影响等问题,这里研究设计了一套LED芯片缺陷的快速检测系统,提出了一种分角度多方向快速卷积、分区统计特征量、支持向量机分类的LED芯片缺陷识别算法,基于QT和Opencv开发了一套LED芯片缺陷快速检测与分类系统,搭建LED芯片检测系统的硬件平台,实现LED芯片实时在线缺陷识别和自动分拣。基于研发的LED芯片缺陷快速检测与分拣系统样机,对LED芯片进行了缺陷分拣的测试。

A Two-Stage Feature Selection Method for Text Categorization by Using Category Correlation Degree and Latent Semantic Indexing

With the purpose of improving the accuracy of text categorization and reducing the dimension of the feature space,this paper proposes a two-stage feature selection method based on a novel category correlation degree(CCD)method and latent semantic indexing(LSI).In the first stage,a novel CCD method is proposed to select the most effective features for text classification,which is more effective than the traditional feature selection method.In the second stage,document representation requires a high dimensionality of the feature space and does not take into account the semantic relation between features,which leads to a poor categorization accuracy.So LSI method is proposed to solve these problems by using statistically derived conceptual indices to replace the individual terms which can discover the important correlative relationship between features and reduce the feature space dimension.Firstly,each feature in our algorithm is ranked depending on their importance of classification using CCD method.Secondly,we construct a new semantic space based on LSI method among features.The experimental results have proved that our method can reduce effectively the dimension of text vector and improve the performance of text categorization.

宋代诗词中的竹家具品类与设计特征研究

文章旨在对宋代竹家具进行系统性的研究。基于文献研究法对宋代诗词中的宋代竹家具进行品类与设计特征研究,通过研究宋代文字记载中的竹家具为切入点,对宋代竹家具进行了系统归纳和整理;并对宋代竹家具进行品类分析,提出宋代竹家具至少有50余种。进而归纳总结提出文字记载中宋代竹家具具有系统的品类、风雅的设计、“通”“专”的功能、圆竹与围合工艺、独特的审美、文化的隐喻六大设计特征。

两类特征在红外目标识别中的应用

针对目标红外图像类特征选择问题,文中提出将类特征分为类间特征与类内特征两种,分别用于目标识别时的粗判与细判,与传统的特征选择与目标识别均只在类间特征基础上相比,该方法能较好地应用于红外目标的识别。

汉英副词性关联词语篇章衔接功能比较

探究语言表面的同和内部的异、表面的异和内部的同,是我们进行跨文化比较研究、跨语言对比研究的目的之一。文章主要从副词性关联词语篇章衔接的语义与类别和衔接的特点与差异等方面,对汉英副词性关联词语的篇章衔接功能进行对比分析,探讨汉英副词性关联词语篇章衔接功能的共同特点、规律及其差异,旨在揭示汉、英语语言的类型学特点。

网络德育管理模式探讨

网络德育管理模式是指在一定思想理论的指导下,经管理实践而定型的网络德育管理结构及其实施策略。构建网络德育管理模式,既是构建一个指导性的结构范式,又是构建一种管理理论、管理方法。网络德育管理模式的构建应当坚持团队管理原则、智能管理原则、信息共享原则和效益原则。网络德育管理模式主要有阵地资料模式、即时更新模式、参与互动模式、信息素养教育模式和综合模式。

基于邻接区域交叠概率的特征选择方法

针对传统特征选择判据计算量大、需要先验知识以及应用效果不佳的缺点,根据分类错误通常发生在类别之间的邻接区域(贝叶斯决策分界面将穿过该邻接区域)的特点,提出基于邻接区域交叠概率的特征选择判据。该判据通过计算案例样本点落在类别邻接区域中的概率来选择特征,具有从样本中能直接计算并且选择出多个特征组合等优点。通过对标准机器学习数据集WINE的实际应用表明,该判据选择出的特征组合的聚类效果明显好于类内类间判据选择出的特征组合。对轴承故障数据进行特征选择时,该判据能提供多种多个特征组合供选择,其选择的垂直和水平振动特征组合符合工程应用的实际需要,远好于类内类间判据选择的特征组合。

社区神经症的临床特征与亚型分类探讨

目的 为进一步修订我国精神疾病诊断分类标准提供基础资料。方法 使用《精神状况评定和分类》中有关神经症条目内容５４项，社会功能评定量表（ＳＤＳＳ）等评定量表符合ＣＣＭＤ－２－Ｒ及ＩＣＤ－１０诊断的３６５例神经症患者，对其临床特征分类诊断和社会功能进行比较分析。结果①９５％左右的患者以神经症性症状群为主，焦虑性神经症、抑郁性神经症、躯体化障碍、神经衰弱为最多见类型，有３２．２６％的抑郁性神经症婚姻状况不良。②症状严重度以抑郁性神经症和癔症排前２位，神经衰弱最后，社会功能受累也有类似趋势。结论 建议再修订ＣＣＭＤ时，注意以下两点：①保留神经衰弱及躯体化障碍分类诊断；②在亚型的等级梯度上抑郁性神经症放在癔症之前。

面孔识别中脑电成分N170的研究概述

N170是在面孔刺激呈现后的130-200ms记录到的并在160～170ms时达到峰值的一种脑电负成分。目前,在N170的研究中存在争议性的问题有:N170反映面孔结构编码还是面孔特征编码;N170是否是面孔特异性成分;以及N170是否受注意的影响等。这些争议也为N170后续研究指明了方向,即探讨结构编码和特征编码在诱发N170成分上起着怎样的作用;比较不同熟悉度的刺激材料所诱发的N170反应差异;探讨N170成分与识别电位间的关系;采用多研究方法的结合从不同层面深入揭示N170的认知机制。