An exemplification of the Evolving Process by Studying the Chinese Character Mao

This paper tries to demarcate the evolving process of decategorization into three periods and exemplify it by studying the Chinese character Mao.The exemplification shows the correctness of the demarcation.

INFLUENCE OF NON-PROCESS ELEMENTS ON REED PULP'S CHARACTERS DURING OXYGEN DELIGNIFICATION

A series of Reed Pulps were prepared in which the level of Non-Process Elements(NPEs), including calcium, manganese,copper,iron were seclectively enriched and depleted, these pulps were then oxygen delignification,and the pulps were characterized according to kappa number,viscosity,brightness. The results indicated that the enrichment of NPEs have an important effulence on delignification,pulp viscosity and brightness, iron is the most harmful during oxygen delignification but manganese is just like a kind of aid and can enhance brightness and delignification.

The early Japanese books reorganization by combining image processing and deep learning

Many early Japanese books record a large amount of information,including historical politics,economics,culture,and so on,which are all valuable legacies.These books are waiting to be reorganized at the moment.However,a large amount of the books are described by Kuzushiji,a type of handwriting cursive script that is no longer in use today and only readable by a few experts.Therefore,researchers are trying to detect and recognise the characters from these books through modern techniques.Unfortunately,the characteristics of the Kuzushiji,such as Connect-Separate-characters and Manyvariation,hinder the modern technique assisted re-organisation.Connect-Separatecharacters refer to the case of some characters connecting each other or one character being separated into unconnected parts,which makes character detection hard.Manyvariation is one of the typical characteristics of Kuzushiji,defined as the case that the same character has several variations even if they are written by the same person in the same book at the same time,which increases the difficulty of character recognition.In this sense,this paper aims to construct an early Japanese book reorganisation system by combining image processing and deep learning techniques.The experimentation has been done by testing two early Japanese books.In terms of character detection,the final Recall,Precision and F-value reaches 79.8%,80.3%,and 80.0%,respectively.The deep learning based character recognition accuracy of Top3 reaches 69.52%,and the highest recognition rate reaches 82.57%,which verifies the effectiveness of our proposal.

Structural recognition of ancient Chinese ideographic characters

Ancient Chinese characters, typically the ideographic characters on bones and bronze before Shang Dynasty(16th—11th century B.C.), are valuable culture legacy of history. However the recognition of Ancient Chinese characters has been the task of paleography experts for long. With the help of modern computer technique, everyone can expect to be able to recognize the characters and understand the ancient inscriptions. This research is aimed to help people recognize and understand those ancient Chinese characters by combining Chinese paleography theory and computer information processing technology. Based on the analysis of ancient character features, a method for structural character recognition is proposed. The important characteristics of strokes and basic components or radicals used in recognition are introduced in detail. A system was implemented based on above method to show the effectiveness of the method.

Orthographic Processing of Developmental Dyslexic Children in China: Evidence from an Event-Related Potential Study

Objective:This study aimed to explore the orthographic processing of simplified Chinese characters in developmental dyslexic children in Kashgar,Xinjiang,China,and provide a theoretical basis for intervention strategies for developmental dyslexia in Chinese.Methods:Using event-related potential(ERP)measures,18 developmental dyslexic children and 23 typically developing children performed a character decision task with three types of stimuli:real characters(RCs),pseudocharacters(PCs),and noncharacters(NCs).Results:Behavioral results showed that the control children displayed a faster and higher accurate performance than the dyslexic children across PCs and NCs.ERP data revealed that the RCs and PCs elicited a stronger P200 than the NCs.Compared with the RCs and NCs,children in the control group showed more N400 negatives for PCs.It is worth mentioning that dyslexic children did not show any difference on N400,which reflected the insufficient orthographic processing of dyslexic children in China.Conclusion:These results show that Chinese dyslexic children had orthographic processing defects.

Automated Handwriting Recognition and Speech Synthesizer for Indigenous Language Processing

In recent years,researchers in handwriting recognition analysis relating to indigenous languages have gained significant internet among research communities.The recent developments of artificial intelligence(AI),natural language processing(NLP),and computational linguistics(CL)find useful in the analysis of regional low resource languages.Automatic lexical task participation might be elaborated to various applications in the NLP.It is apparent from the availability of effective machine recognition models and open access handwritten databases.Arabic language is a commonly spoken Semitic language,and it is written with the cursive Arabic alphabet from right to left.Arabic handwritten Character Recognition(HCR)is a crucial process in optical character recognition.In this view,this paper presents effective Computational linguistics with Deep Learning based Handwriting Recognition and Speech Synthesizer(CLDL-THRSS)for Indigenous Language.The presented CLDL-THRSS model involves two stages of operations namely automated handwriting recognition and speech recognition.Firstly,the automated handwriting recognition procedure involves preprocessing,segmentation,feature extraction,and classification.Also,the Capsule Network(CapsNet)based feature extractor is employed for the recognition of handwritten Arabic characters.For optimal hyperparameter tuning,the cuckoo search(CS)optimization technique was included to tune the parameters of the CapsNet method.Besides,deep neural network with hidden Markov model(DNN-HMM)model is employed for the automatic speech synthesizer.To validate the effective performance of the proposed CLDL-THRSS model,a detailed experimental validation process takes place and investigates the outcomes interms of different measures.The experimental outcomes denoted that the CLDL-THRSS technique has demonstrated the compared methods.

Handwritten Numeric and Alphabetic Character Recognition and Signature Verification Using Neural Network

Handwritten signature and character recognition has become challenging research topic due to its numerous applications. In this paper, we proposed a system that has three sub-systems. The three subsystems focus on offline recognition of handwritten English alphabetic characters (uppercase and lowercase), numeric characters (0 - 9) and individual signatures respectively. The system includes several stages like image preprocessing, the post-processing, the segmentation, the detection of the required amount of the character and signature, feature extraction and finally Neural Network recognition. At first, the scanned image is filtered after conversion of the scanned image into a gray image. Then image cropping method is applied to detect the signature. Then an accurate recognition is ensured by post-processing the cropped images. MATLAB has been used to design the system. The subsystems are then tested for several samples and the results are found satisfactory at about 97% success rate. The quality of the image plays a vital role as the images of poor or mediocre quality may lead to unsuccessful recognition and verification.

航空发动机叶片装配执行过程智能检测及AR引导

为了提高航空发动机叶片装配执行过程的作业智能化程度,本文提出一种航空发动机叶片装配执行过程智能检测及AR引导方法,该方法包括叶片编码识别、物料AR出入库和齐套摆放过程状态检测3个环节。针对航空发动机叶片物料缺乏自动化识别和智能化纠错的问题,搭建基于编码识别的叶片物料管理架构,提出基于图像处理的叶片编码图像前处理增强操作,并利用贝叶斯纠错对识别结果进行正误判断和纠错校正的后处理操作,提高了叶片编码识别准确率;在物料人工出入库环节,利用AR增强可视化信息辅助用户快速执行作业任务,降低了叶片物料选取作业的时间;针对叶片物料齐套准备过程,构建了基于检测比对的防错纠错系统,避免发生人为错误。所提叶片装配执行过程智能检测及AR增强辅助引导方法可以有效减少人力物力和时间消耗,在推动航空发动机迈向智能化和自动化生产上起到技术支撑作用。

基于图形处理技术的文字符号商用研究

随着计算机技术的普及使用,图形处理技术在各行各业得到推广与应用,其中以互联网行业的应用最为广泛。图形处理技术在文字符号商业标志设计中的应用,不仅要平衡表现效果与艺术美感,还要从用户的角度思考,考虑如何提高商业标志的吸引力、改善用户的阅读感官体验。文字符号商业标志设计需要充分利用先进技术,将各种网络资源整合起来,提高信息传达与表现效果,从而提高商业标志的美感。图形处理技术的应用,主要是为了丰富商业标志内容、提高艺术美感,从而吸引更多的网民点击和浏览,改善网民的阅读体验。因此,需要重视图形处理技术的合理应用。

基于综合强度指数和受灾面积的区域性暴雨过程的客观识别及特征分析

本文提出一种基于综合强度指数和受灾面积的区域性暴雨过程客观识别标准确定方法。以河北省为例,利用1971~2020年河北省142个国家气象观测站逐日降水数据,选取最大过程降水量、最大日降水量、影响范围、持续时间、平均强度和平均范围为评估指标,构建了6种区域性暴雨过程综合强度指数模型;基于综合强度指数与受灾面积相关性分析,判定最优的综合强度评估方法,进而确定了河北省区域性暴雨过程的最优客观识别标准。利用本标准对河北省区域性暴雨过程进行识别,并分析时空变化特征。结果表明:本方法可有效确定区域性暴雨过程识别标准,识别结果与灾情资料具有较好的一致性和相关性,适用于区域性暴雨过程客观化识别,易于应用推广。1971~2020年,河北省共发生区域性暴雨过程252次,年均5.0次,发生次数呈下降趋势,综合强度指数呈不明显下降;区域性暴雨过程4~11月均有发生,主要集中在7、8月,出现次数占全年总次数的77%;区域性暴雨过程发生站次分布呈西北少东南多的趋势,主要集中在中部、南部和环渤海地区,西北地区最少。

基于关键轮廓点的汉字矢量化及字库生成研究

古迹文字字符数字化保护过程中一直存在无规范流程、准确率低、耗时耗力等问题。针对这种情况,提出古迹文字矢量化流程,其中包括多阶段图像处理与矢量化文字提取两个关键技术。多阶段图像处理技术采用非局部均值去噪算法去除电子噪声和光照不均引入的噪声,运用直方图均衡化技术增强图像对比度,采用基于偏微分方程的Inpainting技术实现区域修复,完成受损字符图像修复。矢量化文字提取技术基于汉字笔画的特性提取更多类型的关键轮廓点,去除了冗余的轮廓点和噪声轮廓点。实验表明,本文提出的矢量化文字提取技术相较于现有方法,内存减少8.0419%,与原图相比误差小于0.15,最终提取到的字符存储到了Unicode字库中,可在文本编辑器中使用。

融合网络安全领域知识的Web指纹智能识别

针对Web指纹识别能力有限的问题,提出了一种融合网络安全领域知识的Web指纹识别方法。通过构建网络安全领域的术语词典和使用预训练的专业领域词向量,将网络知识与Web指纹识别能力相融合,并合理利用了白字符和网页中的特殊关键字对识别结果进行优化。实验结果表明,多种模型在融合网络安全知识的策略以后,平均识别准确率从75.1%提高到82.5%、召回率从62.3%提高到75.7%、F1值从0.683提高到0.790。

人工智能数据采集在慢性乙型肝炎患者真实世界研究中的应用

目的:开发一套慢性乙型肝炎(乙肝)的人工智能(AI)数据采集工具,以解决传统的多中心数据采集效率低下的问题。方法:基于国际通用的数据标准,将AI的文字识别和自然语言处理应用于慢性乙肝真实世界队列研究数据采集,实现多种格式数据(包括图片格式的原始数据)的电子化采集、结构化处理,随后自动将数据填入研究电子数据采集(REDCap)系统中的电子病历报告表(eCRF)。结果:AI工具辅助数据采集与纯人工数据采集具有相同的平均准确率,均达到98.66%(P=0.23),但前者所需时间减少75.49%(P<0.05)。结论:本研究开发的AI数据采集工具可显著提高研究数据采集效率,为真实世界研究数据采集提供了新的模式。

基于深度学习的轮胎压印字符检测与识别

在自动化装配线上轮胎与轮毂装配时,需要检测与识别轮胎表面的压印字符串,从而得到轮胎的品牌、型号、尺寸以及生产的年周号等信息,用以管理轮胎信息以及监控轮胎的流向。针对轮胎表面压印字符串的检测与识别问题,文章提出一种基于深度学习的轮胎胎面关键字符的检测与识别方法,搭建了可编程逻辑控制器(programmable logic controller,PLC)、工控机和工业相机的自动化检测与识别平台,通过霍夫变换及坐标变换对采集后的图像进行预处理,采用改进的更快速的区域卷积神经网络(faster region-based convolutional neural network,Faster RCNN)算法为基础检测出目标字符串位置,再通过卷积递归神经网络(convolutional recurrent neural network,CRNN)对检测出的目标字符串进行识别,同时利用编码规则校验识别结果,以提升识别结果的准确率。实验结果表明,改进后的算法在进行轮胎压印字符串的检测与识别时其准确率超过97.0%,满足工业生产应用需求。

基于字词融合的低词汇信息损失中文命名实体识别方法

中文命名实体识别(CNER)任务是一种自然语言处理技术,旨在识别文本中具有特定类别的实体,如人名、地名、组织机构名等,它是问答系统、机器翻译、信息抽取等自然语言应用的基础底层任务。由于中文不具备类似英文这样的天然分词结构,基于词的NER模型在中文命名实体识别上的效果会因分词错误而显著降低,基于字符的NER模型又忽略了词汇信息的作用,因此,近年来许多研究开始尝试将词汇信息融入字符模型中。WC-LSTM通过在词汇的开始字符和结束字符中注入词汇信息,使模型性能获得了显著的提升。然而,该模型依然没有充分利用词汇信息,因此在其基础上提出了基于字词融合的低词汇信息损失NER模型LLL-WCM,对词汇的所有中间字符融入词汇信息,避免了词汇信息损失。同时,引入了两种编码策略平均(avg)和自注意力机制(self-attention)以提取所有词汇信息。在4个中文数据集上进行实验,结果表明,与WC-LSTM相比,该方法的F1值分别提升了1.89%,0.29%,1.10%和1.54%。

面向信息处理的《古籍汉字分级字表(7000字)》的研制

分级字表是文本难度分级的重要基础资源。文章基于大规模古籍文本语料库,在统计构建《古籍汉字通用字表》的基础上,通过挖掘古籍汉字在字形、字音、字义以及应用层面的11个分级计量特征,设计了古籍汉字学习优先级的分级评价体系,将各汉字表示为基于计量特征学习优先级的字向量,借助相似度计算,实现了古籍汉字学习优先级别的3级划分,得到《古籍汉字分级字表(7000字)》。该字表包括甲级字1516个、乙级字2421个,丙级字3063个,能够从文字层面为古汉语文本难度分级提供必要的基础字表,有助于古汉语文本分级研究,助力面向辞书编撰的语言文字信息化平台建设,进而为古文读写能力水平测试、古文篇章的自动分级等研究奠定基础。

豫中地区46份桃种质的观赏性综合评价

【目的】建立桃种质的观赏性综合评价体系,为豫中地区合理引进和应用观赏桃种质资源提供参考依据。【方法】对46份桃种质的花期长度及其树型、花色、花型、花径、单枝着花数等性状指标进行调查,采用层次分析法构建模型,确定指标权重,综合评价其观赏性。【结果】在11个评价指标中,花期长度、花色、单枝着花数、花型、花径所占权重比例均较大(>12%),是影响桃种质观赏价值的重要指标。在赋分标准中,开张树型、牡丹花型、红花、大花径、高花量、长花期分值较高,均为5分。46份桃种质花色繁多,主色以红、粉、白为基调,并有少量复色花种质;植株在豫中地区始花期集中于2月下旬至3月中下旬,嫣粉娇香花期最长,为28 d,而绯桃和玲珑粉花期最短,只有15 d;嫣红早花的单枝着花数最高,平均为48.33朵;黄金美丽的平均花径最大,为53.41 mm,且其花丝数也最多,平均达到89.67条;万重粉的花瓣数是其他种质的2~5倍,平均高达103.33枚。根据综合得分,将46份桃种质划分为4个等级,其中Ⅰ级7份,Ⅱ级17份,Ⅲ级11份,Ⅳ级11份。【结论】利用层次分析法对桃种质的观赏性进行评价,较为系统全面、准确有效。鄢红、嫣红早花、红菊花桃、满天红、五宝桃、万重粉和画春寿星7份种质得分均高于3.677 1,观赏价值较高、适应性强,是园林应用和杂交育种的优良植物材料。

麸炒枳实炮制前后性状及显微鉴别研究

目的对炮制前后的麸炒枳实进行性状及显微鉴别,分析其炮制品与生品的质量差异,为麸炒枳实的炮制机制研究提供参考。方法按照2020版《中华人民共和国药典》枳实麸炒炮制方法和质量标准检测要求,从性状、显微、水分、浸出物和灰分等多方面比较麸炒枳实与生品之间的质量差异。结果枳实生品和麸炒品的水分平均含量分别为11.820%、8.700%,浸出物平均含量分别为48.450%、54.700%,平均灰分分别为6.350%、6.480%。其中麸炒枳实水分含量降低、浸出物含量有所增加,而灰分含量与生品差异较少,但三项指标均符合《中华人民共和国药典》规定。二者在性状、显微和薄层鉴别等方面,均未发现明显改变。结论研究所建立的麸炒枳实炮制前后质量评价标准合理可行,可为麸炒枳实炮制机制研究提供数据支撑。

早花露地小菊杂交后代株系的综合评价与筛选

【目的】建立早花露地小菊综合评价体系,筛选出花期为9月中旬至10月中旬、花色接近纯正红色或黄色、花型和株型等综合表现优异的株系,丰富华北地区早秋节庆用花。为进一步培育早花露地小菊新品种提供育种材料和选择方法。【方法】以露地小菊3个早花亲本和1个重瓣性高且花色鲜艳亲本及其25个经田间初选的杂交后代无性株系为供试材料,划分花色、花期、花型、株型和叶型5个层次,从观测的30个原始性状中筛选出18个关键性状指标作为评价因子,利用层次分析法及K-Means聚类分析法建立综合评价体系,开展杂种株系的综合筛选。【结果】以早花、花型圆整丰满、红黄两种花色稳定为育种目标确定了层次分析法各性状的权重值,其中初花期起始时间(0.1817)权重值最大,其次是花色稳定性(0.1695)、盛花期单株着花数量(0.1383)、开花持续时间(0.1301),再次是花色适宜度(0.0565)、盛花期着花整齐度(0.0565)、花序直径(0.0539)、舌状花数量(0.0574)。通过K-Means聚类分析法将29份材料划分为3个等级,Ⅰ级10个(34.38%),Ⅱ级11个(37.93%),Ⅲ级8个(27.59%);杂交子代性状分离广泛,花色、花型和株型均出现了超亲性状,但花期仍以2个早花亲本初花期最早,符合前人研究认为花期为多基因控制的数量性状。因此,早花露地小菊育种仍需筛选更多亲本,增加可早秋使用的露地小菊资源。【结论】本评价方法综合各类评价体系优势,以数量化结果充分表征田间观测的性状特征,以育种目标为导向进行层次分析赋值评价,为综合评价和筛选早花露地小菊优良品种提供了量化方法。建立的评价体系可以很好地对早花露地小菊进行评价与筛选,为后期早花露地小菊新品种的选育提供了一种可靠方法,有助于提高后代优良种质筛选效率。最终筛选出10个观赏性状优良的早花露地小菊株系,可作为新品种或新品系应用,也可作为育种材料。

社会信息加工视角下网络舆情群体智慧涌现机理

[研究目的]厘清网络舆情群体智慧涌现机理可洞察复杂舆情场内的信息价值,为网络舆情的知识管理提供理论依据。[研究方法]在群体智慧涌现的性态刻画基础上,基于社会信息加工理论构建机理集成模型,包括认知触发机理、协同标注机理、观点交互对抗机理、时效控制机理、平台响应机理,并讨论子机理间的作用关系,通过实证分析证明机理研究的合理性。[研究结论]网络舆情群体智慧涌现具有复杂非线性系统特征,知识实体符合幂律分布规律。网络群体在舆情观点发散阶段的知识密度较高,在舆情观点聚合阶段群体智慧涌现并不明显,观点分散造成的信息异质环境并不是激发群体智慧的充要条件。