Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

Transductive Transfer Dictionary Learning Algorithm for Remote Sensing Image Classification

To create a green and healthy living environment,people have put forward higher requirements for the refined management of ecological resources.A variety of technologies,including satellite remote sensing,Internet of Things,artificial intelligence,and big data,can build a smart environmental monitoring system.Remote sensing image classification is an important research content in ecological environmental monitoring.Remote sensing images contain rich spatial information andmulti-temporal information,but also bring challenges such as difficulty in obtaining classification labels and low classification accuracy.To solve this problem,this study develops a transductive transfer dictionary learning(TTDL)algorithm.In the TTDL,the source and target domains are transformed fromthe original sample space to a common subspace.TTDL trains a shared discriminative dictionary in this subspace,establishes associations between domains,and also obtains sparse representations of source and target domain data.To obtain an effective shared discriminative dictionary,triple-induced ordinal locality preserving term,Fisher discriminant term,and graph Laplacian regularization termare introduced into the TTDL.The triplet-induced ordinal locality preserving term on sub-space projection preserves the local structure of data in low-dimensional subspaces.The Fisher discriminant term on dictionary improves differences among different sub-dictionaries through intra-class and inter-class scatters.The graph Laplacian regularization term on sparse representation maintains the manifold structure using a semi-supervised weight graphmatrix,which can indirectly improve the discriminative performance of the dictionary.The TTDL is tested on several remote sensing image datasets and has strong discrimination classification performance.

A robust sparse representation algorithm based on adaptive joint dictionary

Sparse representation based on dictionary construction and learning methods have aroused interests in the field of face recognition.Aiming at the shortcomings of face feature dictionary not‘clean’and noise interference dictionary not‘representative’in sparse representation classification model,a new method named as robust sparse representation is proposed based on adaptive joint dictionary(RSR-AJD).First,a fast lowrank subspace recovery algorithm based on LogDet function(Fast LRSR-LogDet)is proposed for accurate low-rank facial intrinsic dictionary representing the similar structure of human face and low computational complexity.Then,the Iteratively Reweighted Robust Principal Component Analysis(IRRPCA)algorithm is used to get a more precise occlusion dictionary for depicting the possible discontinuous interference information attached to human face such as glasses occlusion or scarf occlusion etc.Finally,the above Fast LRSR-LogDet algorithm and IRRPCA algorithm are adopted to construct the adaptive joint dictionary,which includes the low-rank facial intrinsic dictionary,the occlusion dictionary and the remaining intra-class variant dictionary for robust sparse coding.Experiments conducted on four popular databases(AR,Extended Yale B,LFW,and Pubfig)verify the robustness and effectiveness of the authors’method.

Simultaneous denoising and resolution enhancement of seismic data based on elastic convolution dictionary learning

Enhancing seismic resolution is a key component in seismic data processing, which plays a valuable role in raising the prospecting accuracy of oil reservoirs. However, in noisy situations, existing resolution enhancement methods are difficult to yield satisfactory processing outcomes for reservoir characterization. To solve this problem, we develop a new approach for simultaneous denoising and resolution enhancement of seismic data based on convolution dictionary learning. First, an elastic convolution dictionary learning algorithm is presented to efficiently learn a convolution dictionary with stronger representation capability from the noisy data to be processed. Specifically, the algorithm introduces the elastic L1/2 norm as a sparsity constraint and employs a steepest gradient descent strategy to efficiently solve the frequency-domain linear system with substantial computational cost in a half-quadratic splitting framework. Then, based on the learned convolution dictionary, a weighted convolutional sparse representation paradigm is designed to encode the noisy data to acquire an optimal sparse approximation of the effective signal. Subsequently, a high-resolution dictionary with a broadband spectrum is constructed by the proposed parameter scaling strategy and matched filtering technique on the basis of atomic spectrum modeling. Finally, the optimal sparse approximation of the effective signal and the constructed high-resolution dictionary are used for data reconstruction to obtain the seismic signal with high resolution and high signal-to-noise ratio. Synthetic and field dataset examples are executed to check the effectiveness and reliability of the developed method. The results indicate that this method has a more competitive performance in seismic applications compared with the conventional deconvolution and spectral whitening methods.

A weighted block cooperative sparse representation algorithm based on visual saliency dictionary

Unconstrained face images are interfered by many factors such as illumination,posture,expression,occlusion,age,accessories and so on,resulting in the randomness of the noise pollution implied in the original samples.In order to improve the sample quality,a weighted block cooperative sparse representation algorithm is proposed based on visual saliency dictionary.First,the algorithm uses the biological visual attention mechanism to quickly and accurately obtain the face salient target and constructs the visual salient dictionary.Then,a block cooperation framework is presented to perform sparse coding for different local structures of human face,and the weighted regular term is introduced in the sparse representation process to enhance the identification of information hidden in the coding coefficients.Finally,by synthesising the sparse representation results of all visual salient block dictionaries,the global coding residual is obtained and the class label is given.The experimental results on four databases,that is,AR,extended Yale B,LFW and PubFig,indicate that the combination of visual saliency dictionary,block cooperative sparse representation and weighted constraint coding can effectively enhance the accuracy of sparse representation of the samples to be tested and improve the performance of unconstrained face recognition.

基于数据挖掘的痛经用药规律研究

目的:基于数据挖掘技术分析《中医方剂大辞典》中治疗痛经方剂的用药规律。方法:根据纳入与排除标准筛选出《中医方剂大辞典》中治疗痛经的方剂,对其进行方药名称规范,运用Excel、IBM SPSS Modeler 18.0、IBM SPSS Statistics 26.0等进行频数统计、关联规则分析、聚类分析及因子分析。结果:共纳入151首治疗痛经的方剂,涉及中药191味;其中出现频次≥10的中药为当归、川芎、延胡索、白芍、香附、甘草等共36味,药效以补虚药、活血化瘀药、理气药为主,药性偏温、寒、平,药味则偏辛、苦、甘,多入肝、脾、心经。选取出现频次≥25的13味高频药物进行关联规则分析,得到“当归-川芎“”当归-香附-川芎“”当归-熟地黄-白芍-川芎”等常用药对21组,并通过提升关联规则的支持度得到“当归-川芎-白芍”核心组合。进行高频药物聚类分析得出4类药物组合,C1:白芍、熟地黄、川芎、当归;C2:牡丹皮、茯苓;C3:香附、甘草;C4:桃仁、红花,因子分析得到5个公因子。结论《:中医方剂大辞典》中治疗痛经以补血活血、化瘀散结、疏肝理气为主。

基于知识图谱的多特征融合谣言检测方法

为了解决谣言检测中由于缺乏外部知识而导致模型难以感知内隐信息,进而限制了模型挖掘深层信息的能力这个问题,提出了基于知识图谱的多特征融合谣言检测方法(KGMRD)。首先,对于每个事件,将帖子和评论共同构建为一个文本序列,并利用分类器从中提取情感特征,利用ConceptNet基于文本构造其知识图谱,将知识图谱中的实体表示利用注意力机制与文本的语义特征进行聚合,进而得到增强的语义特征表示;其次,在传播结构方面,对于每个事件,基于帖子的传播转发关系构建传播结构图,使用DropEdge对传播结构图进行剪枝,从而得到更有效的传播结构特征;最后,将得到的特征进行融合处理得到一个新的表示。在Weibo、Twitter15和Twitter16三个真实数据集上,使用SVM-RBF等七个模型作为基线进行了对比实验。结果表明:对比当前效果最好的基线,KGMRD方法在Weibo数据集的ACC指标提升了1.1%;在Twitter15和Twitter16数据集的ACC指标上提升了2.2%,证明了KGMRD方法是合理的、有效的。

基于苹果应用商店大数据的APP词典融媒创新分析

手机终端的APP词典是目前最主流、最贴近融媒辞书发展趋势的数字化词典形态。利用Data.ai平台对苹果应用商店2360个APP词典的调查研究发现,自2008年首个产品发行以来,APP词典在数字开发商科技实力的巨大推动下迅速发展,展现了六大融媒创新变革。侧重词典本体内链“融合”的创新表现为:基于语言加工的多语种服务融合,基于集成整合的多词典类型融合,基于逻辑媒体的多模态内容融合和基于查询场景的多检索手段融合;侧重词典外部世界外链“融通”的创新表现为:数字工具之间的功能融通和词典交际主体之间的身份融通。APP词典要实现融媒辞书的跨越转型,还需要考虑:如何在利用技术追求“美颜”式发展的同时做到内外兼修,如何在编纂传统规范和身份创新融通之间实现编用平衡,如何达到海量内容与精准服务的信息平衡,如何保证泛在服务与知识工具的定位平衡。

探寻中国译学话语的发展之路写在《中国译学大辞典》(第二版)出版之前

一部术语史就是一部专业话语的历史。术语是专业话语的基本要素,术语系统的建设是反映相关学科进步和走向的一面镜子。本文概述了《中国译学大辞典》(第二版)的编纂思想,介绍了该辞典的编辑方针、收选原则和编辑实践,从中可以看出我国译学术语的积累与译学话语体系的完善是同步的。这部辞典见证了翻译学科酝酿-诞生-发展的过程。

压缩感知字典迁移重构的小样本轴承故障诊断

针对实际应用中智能轴承故障诊断面临标签样本严重不足的问题,提出一种结合压缩感知、字典学习和迁移的数据增强算法,用于小样本故障诊断研究。首先,利用源域标签数据通过小波包字典学习和优化方法生成特定源域字典,并得到共享表示系数,获取故障内在信息;之后采用少量目标域信号微调共享表示系数,并更新源域字典生成迁移字典;最后通过共享表示系数和迁移字典生成大量具有目标域特征的新样本,实现数据增强。采用常用的深度故障诊断网络对该数据增强算法进行了诊断性能验证,结果表明该方法产生的信号具有故障的有效信息,用于模型训练和识别能够取得较好的诊断性能。该方法为小样本故障诊断问题提出了新的思路。

融媒辞书的理论方法与实践演化

从20世纪80年代电子词典出现,到2019年中国辞书学会提出融媒辞书概念,辞书界认识到,辞书从“多媒体”到“融媒体”的演化,不只是“媒体”量的增加,而是涉及媒体与模态之间和模态与模态之间的融合机制等问题;媒体形式的融合应促进模态内容的融合,释义和注释内容的融合才是融媒辞书的核心。模态是被释义词的指称通过认知感官反映在人们大脑中的心理映像,意义在多模态语境中更易于理解。融媒辞书应根据语词释义的需要,把相关的媒介、媒体、模态融合为一体,使之共同参与语义的表征过程,成为重要的释义元素。本文意在厘清融媒辞书的核心内涵、融合要素、融合路径、模态元素和理论基础,将融媒辞书编纂的文本特征概括为:两极化、数据化、模态化、平台化、网络化;并结合实例,从融媒辞书的制作资源、知识文本和数字技术3个方面来考察现有数字词典的融媒特征如何实现及其状况。

词类标注对词典释义的促进作用

词类标注和词典释义有着密切的关系。从词类标注的视角来审视词典释义,更容易发现释义中以前不易发现的问题,可以使释义更加严谨、更加精细化,从而促进词典释义的质量提高。本文从词目和释义词语的语法属性的一致性、动词和形容词义项的增补、虚词释义的修改、冗余义项的删除四个方面对此予以阐述。每个方面选取若干条目,这些条目都是因词类标注而发现释义存在问题,从而对释义做出了相应的修订。通过对它们的修订来具体说明词类标注对词典释义的促进作用。

中医心理学术语规范化及术语字典构建研究

构建《中医心理学术语字典》,为中医心理学临床诊疗数据挖掘分析提供可参考的规范化术语。通过文献调研、专家咨询的方式获得心理学相关标准,选取其中对症状、病因病机、辨证分型、治法、方药等有明确定义的10部书籍作为原始术语的来源,确定《中医心理学术语字典》分类框架,参照《国际疾病分类第十一次修订本(ICD-11)》及相关国家标准、教材等权威文献整理并规范每类术语,并构建各类术语的首选术语与其他术语的映射。结果显示,形成了由2类15个子类3 056条术语组成的《中医心理学术语字典》,实现了术语编码,其中独特的心理学术语有1 030条,如“百合病”“脏躁病”等。每类术语形成以首选术语为核心,每个术语包括原始术语来源、原始术语、规范后术语、首选术语、首选术语定义、首选术语定义来源6个属性,具有支持数据采集、数据检索、挖掘模型形成、病历模板生成等功能。本研究首次构建了分类比较全面的中医心理学相关术语字典,包括中医及现代医学术语,可满足临床数据向科研数据转化的实际需求,并用于支撑临床电子病历数据采集、数据挖掘分析、新知识发现等。

基于聚类与稀疏字典学习的近似消息传递

基于传统字典学习的近似消息传递(approximate message passing,AMP)算法对训练样本数量的需求较高,且运算成本较高。本文引入双稀疏模型,构建基于稀疏字典学习的AMP框架,降低迭代过程中字典学习对训练样本数量的需求,提高压缩感知图像重建的质量与效率。进一步,提出基于聚类与稀疏字典学习的AMP算法,在迭代过程中依据图像块特征进行分类,并为各类图像块分别学习稀疏字典,实现自适应去噪。与基于传统字典学习的AMP算法相比,基于聚类与稀疏字典学习的AMP算法能够将重建图像的峰值信噪比提高0.20~1.75 dB,并且能够将运算效率平均提高89%。

汉语词典学研究70年

从词典学理论与价值研究、词典编纂与修订研究、词典编纂的个案研究、词典编纂的方法创新四个方面梳理汉语词典学研究70年来的发展脉络,回顾汉语词典学的研究情况,继而得出词典学研究具有以编纂实践为基础、研究阶段特点明显和期刊发表阵地集中的特点,同时分析汉语词典学的研究趋势,并对其未来发展提出若干建议。

《现代汉语词典》数量词标注相关问题考察

文章对《现代汉语词典》(第7版)中标注的42个数量词进行考察,主要从意义和结构的六个方面区分了词和短语,根据功能、参考意义归纳出名词8个、形容词8个、代词3个、副词和助词各1个。文章最后认为数量词不宜单独立类,数量词应归入现代汉语现有词类,而数量短语不应做类型标注。

现代汉语兼类现象批评认知及常态化研究策略

现代汉语兼类现象是一种客观存在于语言事实中动态变化的语法现象。兼类现象的研究往往依赖于《现代汉语词典》,而《现代汉语词典》兼类词词类标注的缺失表明:兼类词只是兼类现象在词典里的部分反映,是兼类现象的子集,二者的不平衡动态关系受到了词典周期性修订、词频的动态监测及对词类活用认知的制约。因此,现阶段兼类现象外延全貌为:词典里的兼类、当下未收录进词典的兼类、活用产生的临时兼类。兼类现象的常态化研究策略可以还原一个更真实的现代汉语兼类情况。

单一模态稀疏字典的压缩感知故障诊断方法

针对压缩感知的算法流程中,传统的稀疏表示字典存在体量庞大、占用内存严重、构造效率低等问题,提出了一种基于单一模态Laplace小波字典的压缩感知故障诊断方法。首先,截取长信号中的特征片段,采用滑动频谱有效值识别信号的共振频带及中心频率,确定Laplace小波的模态频率;其次,根据滑动峭度指标的分布曲线,提取包含单一冲击的片段信号,通过相关滤波法提取Laplace小波的模态阻尼;然后,通过补零插值建立特定长度的冲击原子,并基于循环移位策略,扩张成单一模态的Laplace小波稀疏字典;最后,结合高斯随机测量矩阵和正交匹配追踪算法,实现原始信号的压缩重构。仿真和实验结果表明,单一模态的小波字典具有更高的构造效率,占用内存更低,能够实现数据的有效压缩和特征重构,确保了噪声环境下的故障特征辨识。

《现代汉语词典》名词释义中语言与百科信息的选择与呈现

语文词典与百科词典被认为是两种基本的词典类型。而在词典编纂实践中,语文词典的释义中经常会出现数量不等的百科信息。文章以普通名词中的具体名词类为研究对象,分析它们在《现代汉语词典》相关版本释义中语言信息与百科信息的选择与呈现。文章秉承认知语言学关于词义的观点,认为词义具有百科知识的特点。因此,语文词典释义的语文性与百科性并不是一个“是与否”的性质问题,而是“多或少”的程度问题。词典编纂者需要做的工作是根据释义需要、词典类型以及词典用户的需求,采取“自下而上”的方法,以底层义场为基本模块,确定载义和辨义的必需信息和冗余信息。

基于自适应动态粒子群优化的RAK-SVD方法

K均值奇异值分解(K-SVD)算法是一种行之有效的地震资料去噪方法,但由于其稀疏分解存在不确定性,需要引入正则项对其改进。为此,在常规粒子群算法的基础上,提出了一种自适应动态粒子群算法优化正则化参数的正则化近似K-SVD(RAK-SVD)去噪方法。首先通过修改字典原子和相关参数,解决了由于常规粒子群算法的惯性参数固定不变,导致后期搜索效率下降的问题;其次将正则化系数引入近似K-SVD(AK-SVD)方法,明显提升了去噪效果;最后利用自适应动态粒子群算法自动优选AK-SVD方法中的正则化参数,提高了稀疏分解的确定性,在对强反射信号进行去噪的同时加强了对弱信号的保护。模型测试和实际应用均表明,该方法有利于弱信号的提取和识别,不仅能够显著改善弱地震信号的去噪效果,还提升了计算效率。该方法具有一定的实际应用价值。