共查询到19条相似文献，搜索用时 796 毫秒
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车辆自动识别分类技术是智能运输系统的重要组成部分,它对特定地点和时间的车辆进行识别和分类,并以之作为交通管理、收费、调度、统计的依据.要实现我国公路收费自动化、管理规范科学化,车型自动识别方法的研究势在必行.本文研究基于车型图像代数特征的车型识别方法.该方法首先利用背景差分法从背景图像中提取出运动车辆,并对车型图像进行预处理,然后采用特征并行融合的方法即用PCA方法,最后通过支持向量机分类器进行车型识别. 相似文献
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语音识别技术是通过计算机系统将人类语言的内容及意义进行识别,这个构想始于20世纪50年代。语音识别技术是以语音为研究对象,通过语音信号处理让机器自动识别和理解人类的语言,语音识别技术是一门涉及面很广的学科,与声学、语音学、语言学等都有着密切的联系。近些年来,随着科学技术的快速发展,电子信息产业发展更为全面,其中计算机系统的各方面功能不断强大,"语音识别"技术也取得了突破性进展,其应用范围也融入到各个领域。语音识别技术是以语音为研究对象,通过语音信号处理让机器自动识别和理解人类的语言。 相似文献
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基于认知科学的研究提出一个新颖的计算模型用于物体识别.特征整合理论为计算模型提供了总体路线.基于最大熵原理构建学习过程,获得必要的先验知识构成认知网络.利用认知网络,将底层的图像特征和高层知识捆绑起来.利用条件随机场的基本概念和原理建模捆绑过程.将计算模型应用于现实世界的物体识别,在标准图像库上进行评估,取得了很好的效果. 相似文献
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[目的/意义]从学术共同体的评论性引用视角出发,以引文全文本为基础,结合词频统计、深度学习等方法,探析引文文本中表征突破性评价的文本特征并构建自动识别模型以实现从海量文献中识别潜在突破性文献。[方法/过程]以诺贝尔生理学或医学奖获得者的关键文献以及Science十大科学突破主题的代表文献(医学领域)作为金标准突破性文献集并获取引用语句,对引用语句进行词频统计并结合人工筛选获取表征突破性评价的常用词。对引用语句进行人工标注,利用BERT、BIOBERT模型进行训练形成自动识别模型,并选择癌症领域进行实证分析。[结果/结论]结果表明,学术共同体在评价具有重大突破价值的文献时具有明显的文本特征;相较BERT模型,生物医学语言表示模型BIOBERT对突破性评价引用语句的识别能力明显增强,F1值为0.84。基于引用语句的自动识别模型能够较为精准地识别具有重要学术价值的文献并能在一定程度上实现早期识别和早期评价。 相似文献
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论述了研究网络资源的自动获取识别归档技术的必要性、网络资源的自动获取识别归档技术的关键是对网络资源底层的HTML信息进行自动获取识别归档处理、获取网络资源底层的HTML信息的两种方式、HTML信息的基本架构及架构规律、自动识别网络资源过程中通常遇到的问题及解决办法、实现网络资源自动归档过程中需要处理好的三个问题及解决办法、以C#语言为鳊辑语言，实现网络资源自动获取、自动识别、自动归档的编程思路等。 相似文献
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针对在线医疗社区问答文本复杂程度高、结构化程度低的特点,结合卷积神经网络(CNN)和双向长短期记忆神经网络(BiLSTM)两种深度学习模型以及条件随机场(CRF)模型,提出一套适用于在线医疗问答文本的实体识别方法并进行验证.将问答文本进行清洗和BIO标注后,分别用CNN和BiLSTM进行字级别的特征抽取,将通过两种模型抽取到的特征进行融合后放入CRF中训练出实体预测模型,再将问答文本放入训练好的模型中,得到最终的实体识别结果.以关于乳腺癌疾病问答文本为例,研究结果表明,运用该方法得到的识别结果优于其他模型,且识别准确率达到92.3％、召回率达到89.3％、F值达到90.8％. 相似文献
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[研究目的]高质量专利对促进专利转化、技术追踪和战略布局十分重要,面对海量专利数据,如何准确高效自动识别高质量专利,为开展后续专利投资融资、产业转型等专利工作做基础铺垫,成为当前重要研究问题。[研究方法]以国家知识产权局受理的申请专利为研究对象,使用专利维持年限表征专利质量,提取专利数字特征并嵌入专利文本特征生成的专利-核心词汇网络,搭建图卷积网络模型自动识别高质量专利。[研究结论]目前针对专利质量的研究专注于挖掘专利数字特征而忽视专利文本特征,该方案在高质量专利自动识别过程中使用专利数字特征与文本特征,对当前专利质量研究做出补充。此外,所提方案可在专家标注少量专利文档情况下完成专利质量识别任务,解决现有专利质量标签标注方案无法全面衡量专利质量的局限。同时,将图卷积网络扩展到专利背景下的质量识别领域,为专利质量研究提供崭新框架,实验结果也显示方案具有较高实践价值。 相似文献
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The paper focuses on a particular approach to automatic sentence compression which makes use of a discriminative sequence classifier known as Conditional Random Fields (CRF). We devise several features for CRF that allow it to incorporate information on nonlinear relations among words. Along with that, we address the issue of data paucity by collecting data from RSS feeds available on the Internet, and turning them into training data for use with CRF, drawing on techniques from biology and information retrieval. We also discuss a recursive application of CRF on the syntactic structure of a sentence as a way of improving the readability of the compression it generates. Experiments found that our approach works reasonably well compared to the state-of-the-art system [Knight, K., & Marcu, D. (2002). Summarization beyond sentence extraction: A probabilistic approach to sentence compression. Artificial Intelligence 139, 91–107.]. 相似文献
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Summarisation is traditionally used to produce summaries of the textual contents of documents. In this paper, it is argued that summarisation methods can also be applied to the logical structure of XML documents. Structure summarisation selects the most important elements of the logical structure and ensures that the user’s attention is focused towards sections, subsections, etc. that are believed to be of particular interest. Structure summaries are shown to users as hierarchical tables of contents. This paper discusses methods for structure summarisation that use various features of XML elements in order to select document portions that a user’s attention should be focused to. An evaluation methodology for structure summarisation is also introduced and summarisation results using various summariser versions are presented and compared to one another. We show that data sets used in information retrieval evaluation can be used effectively in order to produce high quality (query independent) structure summaries. We also discuss the choice and effectiveness of particular summariser features with respect to several evaluation measures. 相似文献
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Micro-Doppler-based target detection and feature extraction in indoor and outdoor environments 总被引：1，自引：0，他引：1
In many cases, a target or a structure on a target may have micro-motions, such as vibrations or rotations. Micro-motions of structures on a target may introduce frequency modulation on the returned radar signal and generate sidebands on the Doppler frequency shift of the target's body. The modulation due to micro-motion is called the micro-Doppler (m-D) phenomenon. In this paper, we present an effective quadratic time-frequency S-method-based approach in conjunction with the Viterbi algorithm to extract m-D features. For target recognition applications, mainly those in military surveillance and reconnaissance operations, m-D features have to be extracted quickly so that they can be used for real-time target identification. The S-method is computationally simple, requiring only slight modifications to the existing Fourier transform-based algorithm. The effectiveness of the S-method in extracting m-D features is demonstrated through the application to indoor and outdoor experimental data sets such as rotating fan and human gait. The Viterbi algorithm for the instantaneous frequency estimation is used to enhance the weak human m-D features in relatively high noise environments. As such, this paper contributes additional experimental m-D data and analysis, which should help in developing a better picture of the human gait m-D research and its applications to indoor and outdoor imaging and automatic gait recognition systems. 相似文献
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Ozkan Aslan Serkan Gunal Bekir Taner Dincer 《Information processing & management》2018,54(6):1262-1276
Chunking is a task which divides a sentence into non-recursive structures. The primary aim is to specify chunk boundaries and classes. Although chunking generally refers to simple chunks, it is possible to customize the concept. A simple chunk is a small structure, such as a noun phrase, while constituent chunk is a structure that functions as a single unit in a sentence, such as a subject. For an agglutinative language with a rich morphology, constituent chunking is a significant problem in comparison to simple chunking. Most of Turkish studies on this issue use the IOB tagging schema to mark the boundaries.In this study, we proposed a new simpler tagging schema, namely OE, in constituent chunking for Turkish. “E” represents the rightmost token of a chunk, while “O” stands for all other items. In reference to OE, we also used a schema called OB, where “B” represents the leftmost token of a chunk. We aimed to identify both chunk boundaries and chunk classes using the conditional random fields (CRF) method. The initial motivation was to employ the fact that Turkish phrases are head-final for chunking. In this context, we assumed that marking the end of a chunk (OE) would be more advantageous than marking the beginning of a chunk (OB). In support of the assumption, the test results reveal that OB has the worst performance and OE is significantly a more successful schema in many cases. Especially in long sentences, this contrast is more obvious. Indeed, using OE means simply marking the head of the phrase (chunk). Since the head and the distinctive label “E” are aligned, CRF finds the chunk class more easily by using the information contained in the head. OE also produced more successful results than the schemas available in the literature.In addition to comparing tagging schemas, we performed four analyses. Along with the examination of window size, which is a parameter of CRF, it is adequate to select and accept this value as 3. A comparison of the evaluation measures for chunking revealed that F-score was a more balanced measure in contrast to token accuracy and sentence accuracy. As a result of the feature analysis, syntactic features improves chunking performance significantly under all conditions. Yet when withdrawing these features, a pronounced difference between OB and OE is forthcoming. In addition, flexibility analysis shows that OE is more successful in different data. 相似文献
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Neelakshi Sarma Sanasam Ranbir Singh Diganta Goswami 《Information processing & management》2019,56(1):151-166
With the explosion of multilingual content on Web, particularly in social media platforms, identification of languages present in the text is becoming an important task for various applications. While automatic language identification (ALI) in social media text is considered to be a non-trivial task due to the presence of slang words, misspellings, creative spellings and special elements such as hashtags, user mentions etc., ALI in multilingual environment becomes even more challenging task. In a highly multilingual society, code-mixing without affecting the underlying language sense has become a natural phenomenon. In such a dynamic environment, conversational text alone often fails to identify the underlying languages present in the text. This paper proposes various methods of exploiting social conversational features for enhancing ALI performance. Although social conversational features for ALI have been explored previously using methods like probabilistic language modeling, these models often fail to address issues related to code-mixing, phonetic typing, out-of-vocabulary etc. which are prevalent in a highly multilingual environment. This paper differs in the way the social conversational features are used to propose text refinement strategies that are suitable for ALI in highly multilingual environment. The contributions in this paper therefore includes the following. First, this paper analyzes the characteristics of various social conversational features by exploiting language usage patterns. Second, various methods of text refinement suitable for language identification are proposed. Third, the effects of the proposed refinement methods are investigated using various sentence level language identification frameworks. From various experimental observations over three conversational datasets collected from Facebook, Youtube and Twitter social media platforms, it is evident that our proposed method of ALI using social conversational features outperforms the baseline counterparts. 相似文献
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文章介绍了利用TI公司TMS320VC5416PGE-160DSP芯片设计的一款指纹门禁系统,包括指纹采集模块、电源管理模块、时钟模块、数据存储及传输模块。在开发出自动指纹识别系统的硬件平台上再和外围单片机进行通讯,通过单片机完成对系统指纹识别模块的控制,实现指纹的采集、比对、存储,通过控制键盘实现人机交互。通过上位机软件完成对系统参数的设置、指纹数据的导入,具有指纹添加、删除功能。 相似文献
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In this paper, we propose a novel approach to automatic generation of summary templates from given collections of summary articles. We first develop an entity-aspect LDA model to simultaneously cluster both sentences and words into aspects. We then apply frequent subtree pattern mining on the dependency parse trees of the clustered and labeled sentences to discover sentence patterns that well represent the aspects. Finally, we use the generated templates to construct summaries for new entities. Key features of our method include automatic grouping of semantically related sentence patterns and automatic identification of template slots that need to be filled in. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We apply our method on five Wikipedia entity categories and compare our method with three baseline methods. Both quantitative evaluation based on human judgment and qualitative comparison demonstrate the effectiveness and advantages of our method. 相似文献