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.

甲骨文词典编纂的创造性转化与创新性发展

甲骨文词典传统编纂体例存在受众群体有限、蕴含殷商社会文化成分较少、不利于展开专题研究等问题,再结合主题分类词典从古至今的发展应用情况,不难发现甲骨文领域中仅赵诚的《甲骨文简明词典——卜辞分类读本》是按主题分类编排的,但其分类标准并不纯粹。因此,急需编纂一部词汇学意义上的、按主题分类编排的甲骨文词典。新时代的甲骨文词典的编纂,需在习近平文化思想指引下,从编纂体例、编纂方法、成果产出、数据库建设等四个方面坚持守正创新,推动中华优秀传统文化创造性转化与创新性发展,采用人机协同的方法深度解析甲骨刻辞;应注重多成果产出,在编纂词典的同时,系联出主题分类词表、类纂和同义词词典;建立智能图文检索数据库,解决现有数据库不支持图片检索的难题,实现语言学、计算机技术和甲骨文研究的跨学科高度融合。

融入实体翻译的汉越神经机器翻译模型

在汉越低资源翻译任务中,句子中的实体词准确翻译是一大难点。针对实体词在训练语料中出现的频率较低,模型无法构建双语实体词之间的映射关系等问题,构建一种融入实体翻译的汉越神经机器翻译模型。首先,通过汉越实体双语词典预先获取源句中实体词的翻译结果;其次,将结果拼接在源句末端作为模型的输入,同时在编码端引入“约束提示信息”增强表征;最后,在解码端融入指针网络机制,以确保模型能复制输出源端句的词汇。实验结果表明,该模型相较于跨语言模型XLM-R(Cross-lingual Language Model-RoBERTa)的双语评估替补(BLEU)值在汉越方向提升了1.37,越汉方向提升了0.21,时间性能上相较于Transformer该模型在汉越方向和越汉方向分别缩短3.19%和3.50%,可有效地提升句子中实体词翻译的综合性能。

中国辞书融合发展的理念与实践

2019年以来,在党中央统一部署下,媒体融合走向纵深发展阶段。受全媒体时代媒体融合理念和技术的影响,辞书学界正积极促进由平面辞书向融媒辞书的转变。文章着重对什么是融媒辞书和辞书融合,辞书为什么要融合发展,辞书如何融合发展三个问题进行讨论,为辞书融合的科学发展提供理论支撑。辞书融合发展是我国从辞书古国走向辞书强国的重要举措,是辞书人在新时代新的文化使命。

The Construction of General Principles about the Exemplification in the Electronic Learner's Dictionary

Dictionary has many functions, in which the function of definition is of very importance because the main purpose of dictionary is providing the entry's meaning information for the readers so that the readers can understand and use the entry-word and the realization of the purpose completely depends on lexicographical definition. However, the function of definition is limited, which need the exemplification to assist it. Therefore, the exemplification becomes very important, too. Good exemplification can assist definition, provide grammatical information, and supplement the information usage and so on. Many researches studied the exemplification of dictionary, its principles and so on. Dictionary changed much with the development of technology and many kinds of electronic dictionaries appeared. Few studies are involved with the new-type dictionary. Based on the general principles of the exemplification in a learner's printed dictionary, it is necessary to construct the general principles about the exemplification in the electronic learner's dictionary.

Seismic data denoising based on learning-type overcomplete dictionaries

The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.

融合情感和常识知识的对话生成模型

随着深度学习技术的发展,开放域对话系统作为人机对话系统的重要分支也得到了快速发展。但目前开放域对话模型生成的回复语句依然存在同理心较差、多样性较低等问题。对此,提出一种融合情感和常识知识的对话生成模型。首先依据情感词典和常识知识图谱获取每个单词对应的常识知识向量,然后将该向量和单词本身的词嵌入向量一同输入编码器中进行编码,接着通过两阶段解码来生成回复语句:第一个解码阶段预测要生成单词的情感强度,并据此获得该单词对应的情感向量,第二阶段解码结合第一阶段编码的结果和已生成单词的词嵌入向量及其对应的常识知识向量作为输入,预测要生成的单词。实验结果表明,该模型生成的回复语句更具同理心和多样性,并且在PPL,BLEU,ACC和DISTINCT等指标上相比基线模型都有一定提升。

数字时代的开放式辞书生活

数字化发展正在塑造语言生活和辞书生活的融媒体化、智能化、开放性、多元性等现代化新样态。文章主要从三个方面探讨如何推动辞书生活的开放式发展:一是数字化转型为当代辞书生活带来的新变化;二是开放科学背景下开放式辞书生活的内涵、目标和挑战;三是根据维基百科、GoldenDict词典、“生僻字征集”微信小程序、对话式AI辅助释义等案例,探索可用于我国数字时代开放式辞书生活的模式和经验。开放式辞书生活有助于形成以辞书为纽带的趣缘群体和文化认同,从现实维度实现中华优秀语言文化的集体构建,在提升我国辞书编纂水平、知识管理与服务水平、文化传播能力和世界辞书影响力等方面做出重要贡献。

Ultrasonic Nondestructive Signals Processing Based on Matching Pursuit with Gabor Dictionary

The success of ultrasonic nondestructive testing technology depends not only on the generation and measurement of the desired waveform, but also on the signal processing of the measured waves. The traditional time-domain methods have been partly successful in identifying small cracks, but not so successful in estimating crack size, especially in strong backscattering noise. Sparse signal representation can provide sparse information that represents the signal time-frequency signature, which can also be used in processing ultrasonic nondestructive signals. A novel ultrasonic nondestructive signal processing algorithm based on signal sparse representation is proposed. In order to suppress noise, matching pursuit algorithm with Gabor dictionary is selected as the signal decomposition method. Precise echoes information, such as crack location and size, can be estimated by quantitative analysis with Gabor atom. To verify the performance, the proposed algorithm is applied to computer simulation signal and experimental ultrasonic signals which represent multiple backscattered echoes from a thin metal plate with artificial holes. The results show that this algorithm not only has an excellent performance even when dealing with signals in the presence of strong noise, but also is successful in estimating crack location and size. Moreover, the algorithm can be applied to data compression of ultrasonic nondestructive signal.

用户视域下外向型在线汉语学习词典评价指标体系构建

外向型汉语学习词典目前存在较严重的质量问题,词典评价研究虽对提高词典质量具有重要意义,但过往研究却存在评价主体单一、评价方法主观、评价内容失衡等诸多问题。为突破词典评价研究瓶颈,提高词典编纂质量,本研究拟构建用户视域下的外向型在线汉语学习词典评价指标体系,构建流程为:首先,从用户需求研究文献、APP Store留言板块、深度访谈等数据源中爬取用户评价语料以构建用户评价语料库,借助内容分析法对语料分析后构建初始评价指标体系。其次,采用德尔菲法对指标体系进行优化。最后,利用层次分析法计算出指标权重。最终确立的评价指标体系包含3个一级指标、19个二级指标及56个三级指标。

Comments on Nigel Wiseman s A Practical Dictionary of Chinese Medicine(Ⅰ)——On the“Word-for-word”Literal Approach to Translation

Comments were made on the ＂word-for-word＂ literal translation method used by Mr. Nigel Wiseman in A Practical Dictionary of Chinese Medicine. He believes that only literal translation can reflect Chinese medical concepts accurately. The so-called ＂word-for-word＂ translation is actually ＂English-word-for- Chinese-character＂ translation. First, the authors of the dictionary made a list of Single Characters with English Equivalents, and then they gave each character of the medical term an English equivalent according to the list. Finally, they made some minor modifications to make the rendering grammatically smoother. Many English terms thus produced are confusing. The defect of the word-for-word literal translation stems from the erroneous idea that a single character constitutes the basic element of meaning corresponding to the notion of ＂word＂ in English, and the meaning of a disyllabic or polysyllabic Chinese word is the simple addition of the constituent characters. Another big mistake is the negligence of the polysemy of Chinese characters. One or two English equivalents can by no means cover all the various meanings of a single character which is a polysemous monosyllabic word. Various examples were cited from this dictionary to illustrate the mistakes.

Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection results.In order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.

Comments on Nigel Wiseman's A Practical Dictionary of Chinese Medicine (Ⅱ) ——On the Use of Western Medical Terms to Express the Concepts of Traditional Chinese Medicine

Mr. Wiseman believes that Western medical terms chosen as equivalents of Chinese medical terms should be the words known to all speakers and not requiring any specialist knowledge or instrumentation to understand or identify, and strictly technical Western medical terms should be avoided regardless of their conceptual conformity to the Chinese terms. Accordingly, many inappropriate Western medical terms are selected as English equivalents by the authors of the Dictionary, and on the other hand, many ready-made appropriate Western medical terms are replaced by loan English terms with the Chinese style of word formation. The experience gained in solving the problems of translating Western medical terms into Chinese when West- ern medicine was first introduced to China is helpful for translating Chinese medical terms into English. However, the authors of the Dictionary adhere to their own opinions, ignoring others＂ experience. The English terms thus created do not reflect the genuine meaning of the Chinese terms, but make the English glossary in chaos. The so-called true face of traditional Chinese revealed by such terms is merely the Chinese custom of word formation and metaphoric rhetoric. In other words, traditional Chinese medicine is not regarded as a system of medicine but merely some Oriental folklore.

An application of matching pursuit time-frequency decomposition method using multi-wavelet dictionaries

In the time-frequency analysis of seismic signals, the matching pursuit algorithm is an effective tool for non-stationary signals, and has high time-frequency resolution and a transient structure with local self-adaption. We expand the time-frequency dictionary library with Ricker, Morlet, and mixed phase seismic wavelets, to make the method more suitable for seismic signal time-frequency decomposition. In this paper, we demonstrated the algorithm theory using synthetic seismic data, and tested the method using synthetic data with 25% noise. We compared the matching pursuit results of the time-frequency dictionaries. The results indicated that the dictionary which matched the signal characteristics better would obtain better results, and can reflect the information of seismic data effectively.

Airborne electromagnetic data denoising based on dictionary learning

Time-domain airborne electromagnetic(AEM)data are frequently subject to interference from various types of noise,which can reduce the data quality and affect data inversion and interpretation.Traditional denoising methods primarily deal with data directly,without analyzing the data in detail;thus,the results are not always satisfactory.In this paper,we propose a method based on dictionary learning for EM data denoising.This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal.In the process of dictionary learning,the random noise is fi ltered out as residuals.To verify the eff ectiveness of this dictionary learning approach for denoising,we use a fi xed overcomplete discrete cosine transform(ODCT)dictionary algorithm,the method-of-optimal-directions(MOD)dictionary learning algorithm,and the K-singular value decomposition(K-SVD)dictionary learning algorithm to denoise decay curves at single points and to denoise profi le data for diff erent time channels in time-domain AEM.The results show obvious diff erences among the three dictionaries for denoising AEM data,with the K-SVD dictionary achieving the best performance.

A Novel Automatic Modulation Classification Method Based on Dictionary Learning

Automatic Modulation Classification(AMC) is an important technology used to recognize the modulation type.A dictionary set was trained via signals with known modulation schemes in cooperative scenarios.Then we classify the modulation scheme of the signals received in the non-cooperative environment according to its sparse representation.Furthermore,we proposed a novel approach called Fast Block Coordinate descent Dictionary Learning(FBCDL).Moreover,the convergence of FBCDL was proved and we find that our proposed method achieves lower complexity.Experimental results indicate that our proposed FBCDL achieves better classification accuracy than traditional methods.

Fast image super-resolution algorithm based on multi-resolution dictionary learning and sparse representation

Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.

Human Action Recognition Based on Supervised Class-Specific Dictionary Learning with Deep Convolutional Neural Network Features

Human action recognition under complex environment is a challenging work.Recently,sparse representation has achieved excellent results of dealing with human action recognition problem under different conditions.The main idea of sparse representation classification is to construct a general classification scheme where the training samples of each class can be considered as the dictionary to express the query class,and the minimal reconstruction error indicates its corresponding class.However,how to learn a discriminative dictionary is still a difficult work.In this work,we make two contributions.First,we build a new and robust human action recognition framework by combining one modified sparse classification model and deep convolutional neural network(CNN)features.Secondly,we construct a novel classification model which consists of the representation-constrained term and the coefficients incoherence term.Experimental results on benchmark datasets show that our modified model can obtain competitive results in comparison to other state-of-the-art models.

Multi-task Joint Sparse Representation Classification Based on Fisher Discrimination Dictionary Learning

Recently,sparse representation classification(SRC)and fisher discrimination dictionary learning(FDDL)methods have emerged as important methods for vehicle classification.In this paper,inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection,we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors.To improve the classification accuracy in complex scenes,we develop a new method,called multi-task joint sparse representation classification based on fisher discrimination dictionary learning,for vehicle classification.In our proposed method,the acoustic and seismic sensor data sets are captured to measure the same physical event simultaneously by multiple heterogeneous sensors and the multi-dimensional frequency spectrum features of sensors data are extracted using Mel frequency cepstral coefficients(MFCC).Moreover,we extend our model to handle sparse environmental noise.We experimentally demonstrate the benefits of joint information fusion based on fisher discrimination dictionary learning from different sensors in vehicle classification tasks.

Precipitation Retrieval from Himawari-8 Satellite Infrared Data Based on Dictionary Learning Method and Regular Term Constraint

In this paper,the application of an algorithm for precipitation retrieval based on Himawari-8 (H8) satellite infrared data is studied.Based on GPM precipitation data and H8 Infrared spectrum channel brightness temperature data,corresponding "precipitation field dictionary" and "channel brightness temperature dictionary" are formed.The retrieval of precipitation field based on brightness temperature data is studied through the classification rule of k-nearest neighbor domain (KNN) and regularization constraint.Firstly,the corresponding "dictionary" is constructed according to the training sample database of the matched GPM precipitation data and H8 brightness temperature data.Secondly,according to the fact that precipitation characteristics in small organizations in different storm environments are often repeated,KNN is used to identify the spectral brightness temperature signal of "precipitation" and "non-precipitation" based on "the dictionary".Finally,the precipitation field retrieval is carried out in the precipitation signal "subspace" based on the regular term constraint method.In the process of retrieval,the contribution rate of brightness temperature retrieval of different channels was determined by Bayesian model averaging (BMA) model.The preliminary experimental results based on the "quantitative" evaluation indexes show that the precipitation of H8 retrieval has a good correlation with the GPM truth value,with a small error and similar structure.