A Theoretic Study on the Translation Model from Relevance Theory

Relevance Theory holds that the language communication of human beings is an ostensive-inferential process of searching for relevance.Translation as a communication across language and culture,involves a dual process of communication among the source language author,the translator and the target language readers,during which the translator infers the communicative intention of the source language author,then by assessing the cognitive environments of the target language readers,makes the optimally relevant translation so that the translation can be equivalent in relevant aspects.

User’s Relevance of PIR System Based on Cloud Models

A new method to evaluate fuzzily user’s relevance on the basis of cloud models has been proposed. All factors of personalized information retrieval system are taken into account in this method. So using this method for personalized information retrieval (PIR) system can efficiently judge multi-value relevance, such as quite relevant, comparatively relevant, commonly relevant, basically relevant and completely non-relevant, and realize a kind of transform of qualitative concepts and quantity and improve accuracy of relevance judgements in PIR system. Experimental data showed that the method is practical and valid. Evaluation results are more accurate and approach to the fact better.

Seismic fragility analysis of bridges by relevance vector machine based demand prediction model

A relevance vector machine(RVM)based demand prediction model is explored for efficient seismic fragility analysis(SFA)of a bridge structure.The proposed RVM model integrates both record-to-record variations of ground motions and uncertainties of parameters characterizing the bridge model.For efficient fragility computation,ground motion intensity is included as an added dimension to the demand prediction model.To incorporate different sources of uncertainty,random realizations of different structural parameters are generated using Latin hypercube sampling technique.Mean fragility,along with its dispersions,is estimated based on the log-normal fragility model for different critical components of a bridge.The effectiveness of the proposed RVM model-based SFA of a bridge structure is elucidated numerically by comparing it with fragility results obtained by the commonly used SFA approaches,while considering the most accurate direct Monte Carlo simulation-based fragility estimates as the benchmark.The proposed RVM model provides a more accurate estimate of fragility than conventional approaches,with significantly less computational effort.In addition,the proposed model provides a measure of uncertainty in fragility estimates by constructing confidence intervals for the fragility curves.

An Analysis of Discourse Production and Cognitive Context Dynamism Based on Relevance-Adaptation Model

Taken discourse production as the research objective,it holds that discourse production is dynamic in human communication.It attempts to analyze the dynamics on the basis of Relevance-adaption model from the perspective of cognitive pragmatics and explain the role of the context dynamics that plays in the discourse production.

A Study on Translation of Chinese Culture-loaded Words from the Per-spective of Relevance Theory

The essay tends to analyze the translation of Chinese Culture-loaded words from the perspective of Relevance Theory.The theory gains its prominence by studying the translation process and transcending the conflicts between literal and free translation. It incorporates recent work in cognitive linguistics, with ostensive-inference as its key model. Under the influence of Relevance theory, the translation of culture-loaded words is reader-oriented. Translators are obliged to help target readers to establish new assumptions to achieve equivalent response.

Implications of Relevance Theory to English Reading

Reading is a style of communication between reader and writer while relevance theory is a new approach to communi cation.Based on relevance theory,we can paraphrase reading as followings:reading comprehension is a process of communica tion between reader and writer;a process of making inference from the ostensive-inference communication;a process of seeking optimal relevance on the part of the reader.

On Puns in English Advertisements--From the Perspective of Relevance Theory

Advertising is almost ubiquitous in today's society.In order to deliver the most effective information to the audience in alimited time and space,the advertisers pay more attention to the use of various language skills to promote sales,and puns are one ofthe most important means.Based on the Relevance Theory,this paper reveals the root cause of the successful communication of theadvertisement through the analysis of the audience's understanding of the advertisements with the pun.The first part of this paper mainly introduces the main concepts of Relevance Theory:the ostensive-inferential communication mod-el,cognitive context and the optimal relevance.The second part introduces the definition,features and functions of puns in EnglishAdvertisements.In the third part,based on the Relevance Theory,the paper analyzes the process of achieving the optimal rele-vance when the pun is regarded as an ostensive stimulus and combined with the cognitive context.Finally,the article concludesthat the use of puns in advertisements can achieve optimal relevance through such a cognitive process.

基于话题决策模型的英文文本切题度计算方法研究

为了对英文作文考试中英文文本的切题度加以判断,提出一套基于话题决策模型的英文文本切题度计算方法。介绍该算法的数据处理流程与话题决策模型的数据处理方法,最后通过WIKI_727K数据集对话题决策模型进行训练与测试,经实验研究发现,所提出的话题决策模型在精确率、召回率等方面具有一定的应用优势,并在ASAP数据集上表现出较为理想的切题度判断效果。

英语作文切题度分析算法

为了在英语作文判阅工作中较为准确地提取出切题句子,提出一套基于关系三元组层次主题树模型的英语作文切题度分析算法。通过主题关系三元组的分布式向量来对作文中的句子加以描述,并以余弦值算法对句子的切题度进行判断。最后通过实验分析的方式来验证该算法的有效性。实验研究发现,所提出的英语作文切题度分析算法能够较为准确地识别并提取出英语作文中的切题句子,具有一定的应用价值。

铁路线路与地形三维模型实时动态融合方法研究

铁路线路与地形三维模型的融合是实现铁路线路三维正向设计的瓶颈之一。线路设计过程中,需要实时更新三维场景。现有的静态融合方法会修改原始的地形数据,且计算量大,耗时过长,无法满足动态交互式设计的需求。对此,提出一种铁路线路与地形三维模型实时动态融合方法。基于铁路三维场景,在每帧显示前动态提取视相关地形瓦片模型和线路三维模型,在内存中基于线程池并行对地形瓦片裁剪,实现二者的实时融合,并构建缓存队列,重复利用融合结果。为了提升单块地形瓦片和线路三维模型的融合效率,提出了“超挖-回填”的融合思想。借助高效的三角形空间网格索引,快速删除融合边界内及相交网格涉及到的三角形,虽然“超挖”部分三角形,但避免了繁杂的三角形定位、调整计算。依据超挖三角形外边界和融合边界构建“回填”区域,再采用剪耳法对“回填”区域重构三角网,实现铁路线路与地形三维模型的无缝融合。基于该方法开发了铁路线路三维设计系统,对一条104.9 km铁路案例进行了实验,结果表明:其显示帧率可以保持在35~45 fps,且能够实时更新三维场景。相较于传统的静态融合方法,该方法将长时间的全局模型融合转化为帧前的局部视相关动态融合,减少了线路方案修改时更新的融合数据量及计算量,从而满足了动态交互式设计的需求,提升了三维正向设计的效率。

基于ERHIs法的雅鲁藏布江流域水文情势变化研究

为探究变化环境下雅鲁藏布江流域环境流量组分与水文情势变化情况,采用改进后生态最相关水文指标ERHIs(The most Ecologically Relevant Hydrologic Indicators)、RVA(Range of Variability Approach)法定量分析了1965—2018年间不同阶段不同环境组分下的雅鲁藏布江流域内水文情势的变化情况,并结合5种突变检验方法对指标进行验证。结果表明,通过EasyDHM插补数据是可行的,参数优化后的Nash系数最大为0.97,最小为0.67,均在合理范围内,在缺资料地区运用较好。针对雅鲁藏布江2个断面强变异指标改变度计算结果均表明雅江中下游地区的水文改变度为高度改变。在指标选取方面,生态水文改变过程的指标体系(Indicators of Hydrologic Alteration,IHA)参数之间的相关性较高,指标间的冗余问题十分突出。ERHIs指标具有一定的合理性,不仅有效缓解了IHA的冗余性问题,还有利于抓住最关键的生态水文变量,对生态水文研究、流域水资源管理和生态保护都具有重要的参考价值和借鉴意义,为短缺水文资料地区的生态评估提供参考。

煤自燃热失重特征规律及关键官能团研究

为了研究煤自燃过程中对失重起重要作用的关键官能团,以3种不同变质程度煤样为研究对象,根据TG热重实验和傅里叶变换红外实验,研究煤自燃热失重过程的特征温度和官能团变化规律,基于频率比模型法和灰色关联度分析煤自燃失重量与官能团间的相关性,并探讨不同变质程度煤自燃热失重过程的关键官能团。研究结果表明:随煤变质程度加深,热失重曲线后移,从褐煤到无烟煤,热失重特征温度分别增加22%,37%;随温度增加,官能团含量突变温度点与特征温度相近,羟基在150℃之前呈指数式下降后趋于稳定,脂肪烃、含氧官能团和芳香烃在燃点温度之后线性下降;影响不同变质程度煤自燃热失重的关键官能团不一致,C C是影响褐煤的热失重关键官能团,而甲基是影响烟煤、无烟煤的热失重关键官能团。研究结果可为煤自燃热失重过程关键官能团的确定提供重要参考。

急性有机磷农药中毒继发肺损伤相关因素-基于随机森林和决策树模型

目的探讨急性有机磷农药中毒(AOPP)继发肺损伤相关因素,制定合理方式措施,减少肺损伤发生风险。方法选取2021年3月至2023年3月南阳市第一人民医院收治的102例AOPP患者作为研究对象,根据发病至入院12 h内肺损伤发生率分为肺损伤组和非肺损伤组,比较两组一般资料、实验室指标等,采用随机森林和决策树模型分析AOPP继发肺损伤影响因素,绘制受试者工作特征(ROC)曲线及曲线下面积(AUC)分析两种模型预测效能。结果102例AOPP患者肺损伤发生率为70.59%(72/105);决策树模型显示:服毒量≥40 mg/kg+急性生理与慢性健康评分(APACHEⅡ评分)≥15分的AOPP患者肺损伤发生率高;服毒量≥40 mg/kg+ChE<450 U/L+转移生长因子β1(TGF-β1)≥10 pg/mL+阿托品化时间≥90 h的AOPP患者肺损伤发生率高。随机森林模型显示,服毒量对AOPP患者继发肺损伤影响程度最高,其次是入院时ChE、入院时TGF-β1、阿托品化时间;ROC曲线显示,随机森林模型预测AOPP继发肺损伤准确度、特异度(88.24%、90.91%)高于决策树模型(67.65%、59.10%)(P<0.05)。结论基于随机森林模型的预测模型建立可准确预测AOPP继发肺损伤,其预测能力优于决策树模型,可协助医务人员进行临床决策,降低肺损伤发生风险。

央行“言行”偏差与金融市场间长期动态相关性——基于混频DCC-MIDAS模型

运用自然语言处理方法构建央行政策操作偏离指数来衡量央行“言行”偏差的程度,并基于混频DCC-MIDAS模型对金融市场间长期动态相关性展开研究。实证结果如下:央行政策操作偏离指数对债券市场收益率波动产生负向影响,其影响主要来源于意外的货币政策紧缩;对股票市场、外汇市场收益率波动具有非对称性影响。针对金融市场间长期动态相关性影响,央行政策操作偏离指数负向影响债券-外汇市场间的长期相关性,尤其对于国债与外汇市场的长期相关性的影响更为显著;对于股票-外汇市场的长期动态相关性,央行政策操作偏离指数对其具有显著的正向影响,其影响主要来源于央行正向的“言行”偏差;央行政策操作偏离指数对于股票-债券市场的长期相关性影响不显著。

数学模型在服装立体设计中的应用

随着时尚行业的快速发展,传统的服装设计已不能适应服装市场对效率、个性化和创新的需求,而数学模型在服装立体设计中的应用,可以优化设计流程,提高设计效率,增强设计的灵活性和创新性。因此,以评价数据相关性分析为例,探讨数学模型在服装立体设计中的应用。

The Application of Inferential Model in Context Selection of Discourse Coherence

The selection of context is not random but limited by linguistic communicators'cognitive environment.Sperber &Wilson propose a more persuasive inferential model on the basis of Grice's theory.They think language communication is a de ductive and inferential process in which the communicators deal with the information in the context,a set of assumptions,and finally infer the meaning.Since a discourse is the work of language and the product of the process of linguistic communication,the inferential model will also adapt to the discourse analysis.With the inferential model as its basic analyzing framework,this paper puts discourse in a dynamic communicative process to investigate how the discourse receiver selects the context to make the com munication succeed.

基于MRMR-DRSN的电力系统暂态稳定评估

随着电力系统的广泛互联互通和相量测量单元(phasor measurement unit,PMU)的广泛应用,电力系统的安全运行面临着巨大挑战。为实现对电力系统运行状态快速、准确、有效的评估,提出了一种基于最大相关-最小冗余(max-relevance and min-redundancy,MRMR)准则和深度残差收缩网络(deep residual shrinkage network,DRSN)的暂态稳定评估方法。首先,利用MRMR准则进行特征选择,并将筛选后的关键特征和相应的类标签作为DRSN模型的输入和输出进行离线训练。然后,制定模型更新机制以应对电力系统运行工况变化。最后,基于PMU实时数据和训练好的DRSN,可立即提供暂态稳定评估结果。在IEEE 10机39节点系统上进行测试,结果表明,所提方法相较于其他数据驱动方法的综合评估性能更优异,同时还具有较好的抗噪性能和鲁棒性。

基于数据驱动模型的反窃电行为精准取证系统

反窃电行为现场蹲守取证的时效性与准确性均较差,导致取证工作效率下降,且取得的证据不完整,设计基于数据驱动模型的反窃电行为精准取证系统。在现场布设反窃电稽查仪、红外摄像头,在采集层获取用户相关用电数据以及视频图像,并通过通信层的集中器将数据传输到主站层。主站层借助数据驱动模型从用电数据以及视频图像中挖掘证据,对获取的证据进行实时整理和归纳,完成反窃电行为精准取证。实验结果表明,利用所设计系统对窃电用户进行检测与识别后所得F1值均大于9,且接近于10,能够实现反窃电行为精准取证。