搜索引擎中数据重复搜集问题的探讨

提高搜索引擎的性能，给用户反馈一个高精度、低重复率的检索结果，就必须要克服搜索引擎对数据的重复搜集问题。对数据的重复搜集问题进行了研究和分析，并有针对性地提出了解决方案。     

搜索引擎综合分析

随着互联网的广泛应用,搜索引擎成为了越来越多的用户从海量信息中获取知识必不可少的工具.说明了搜索引擎当前发展的现状,简述了搜索引擎的分类,基本原理和工作机制,重点分析了搜索引擎的检索技术及页面排序方法,最后介绍了搜索引擎面临的挑战和未来发展趋势.     

Internet上多来源MSDS的统一检索方法

国际上称之为材料安全数据表的MSDS是一份关于化合物安全信息的综合性法律文件.随着人们安全意识的提高以及对职业健康、环境保护等认识的不断深入,MSDS作为一个安全信息文件越来越受重视.由于制作MSDS需要一定的成本,充分利用网络上免费的MSDS数据对于了解化合物的安全信息具有重要的参考价值.已有针对MSDS的搜索工具,一般只能检索单一来源的MSDS数据库,所以建立一个能通过一个查询同时榆索多个来源数据库的MSDS搜索引擎,将为使用MSDS的人员提供极大方便.本文提出建立MSDS统一搜索引擎的框架,通过链接分析技术和深层网数据检索技术获取MSDS文件,然后缓存各数据源检索结果,并建立化合物索引以提高MSDS搜索引擎的响应速度.其实现过程包括发现与自动构造检索式模式、自动获取检索结果页面、利用数据提取的方法获取化合物标识信息以建立MSDS的化合物索引等多个方面,为建立一个可用的MSDS统一搜索引擎奠定坚实基础.     

基于Elasticsearch的知识库检索引擎系统设计与实现

当前,随着大数据的发展,数据包含的价值越来越丰富,如何对海量的数据实现毫秒级的关键字检索越来越被各个信息化系统所需要。因此,文中基于Elasticsearch作为框架,通过知识库检索引擎技术研究及应用实现,形成一套具备海量数据存储、自定义知识库接入、高效全文检索等特点的分布式全文搜索引擎系统解决上述问题。     

不需要索引的检索系统

日本富士通公司以瞬索(XML数据搜索引擎)为代表的瞬索系列软件已发展成为全面基于Internet的包括数据转换、数据加工整理、数据维护、信息发布与检索在内的全线产品。针对图书情报以及各个政府机构、企事业单位文献信息中心信息资源的检索,日本富士通公司特提出基于瞬索(XML数据搜索引擎)的文献信息系统检索系统解决方案。方案简介Interstage Shunsaku Data Manager,中文名称为“瞬索XML数据搜索引擎”(以下简称瞬索),具有高速数据检索、跨越多个数据源的集成检索、便捷的全文检索、对于非结构化数据的检索及不需要索引(no index)等特…     

基于本体的企业搜索引擎研究

随着企业的发展以及企业信息化建设的推进,Internet技术在越来越多的企业中得到应用,企业内部信息量以惊人的速度增长.面对企业内部海量信息,传统的搜索引擎无法满足企业用户对企业信息的检索需求,因此企业级的搜索引擎成为迫切需求.对传统的搜索引擎和本体知识进行学习,设计了一个基于本体的企业级搜索引擎系统模型.该设计运用本体知识,对检索关键词进行语义扩展,对检索结果进行语义相似性判断,最终实现搜索结果更加精确.     

集成搜索引擎的文本数据库选择

用户需要检索的信息往往分散存储在多个搜索多个搜索引擎各自的数据库里,对普通用户而言,访问多个搜索引擎并从返回的结果中分辨出确实有网页是一件费时费力的工作,集成搜索引擎则可以提供给用户一个同时记问多个搜索引擎人集成环境,集成搜索引擎能将其接收到的用户查询提交给底层的多个搜索引擎进行搜索,作为一种搜索工具,集成搜索引擎具有如WEB查询覆盖面比传统引擎更大,引警有更好的可扩展性等优点,讨论了解决集成搜索引擎的数据库选择问题的多种技术,针对用户提交的查询要求,通过数据库选择可以选定最有可能返回有用信息的底层搜索引擎。     

基于本体的搜索引擎模型设计

目前,常用的全文搜索引擎都是基于关键词检索的,其查准率和查全率都较低,并且返回记录太多,重复信息过多,使得搜索引擎的查询效率很低。基于此,提出了一种基于本体的搜索引擎模型,通过提取的文档中概念,确定其所属的领域本体,以此归类,并用文档—概念匹配系数建立索引。搜索时,采用基于概念匹配的方式进行检索,对属于不同领域本体的文档,分类输出。提高搜索引擎的查准率和查全率,减少冗余信息,从而提高搜索引擎的查询效率。     

基于策略爬行与混合索引的医药行业垂直搜索引擎的设计与实现

随着互联网应用的深入,越来越多的用户希望通过搜索引擎获得特定行业的相关信息,通用搜索引擎无法有效地满足相应需求。文中主要介绍医药行业垂直搜索引擎的设计与实现。设计基于智能搜索引擎的架构,采用了任务驱动的聚焦搜索、隐藏搜索技术;字词混合倒排索引及优化的字倒排索引、检索技术。提供了资源收集阶段的可控策略爬行,和高效的索引、检索功能。实现了针对医药行业的高专业度、高准确率、高效率的信息垂直搜索。     

基于UIMA的企业非结构化数据的准确搜索

随着信息技术的不断发展及广泛应用,企业内部的非结构化历史数据越来越多。一方面,企业可利用资源,可借鉴经验不断增加,另一方面,由于当前非结构化数据管理,检索的的技术非常落后,使得企业在非结构化数据管理检索等方面,利用方面举步唯艰。在针对特定领域的垂直搜索引擎的基础上,更加高效的智能化的搜索引擎的研究也就成为发展的必然。论文提出了一种新的非结构化数据检索管理方法,并介绍了实现该方法相关的技术:UIMA和OEE。     

Fault prognosis of filamentous sludge bulking using an enhanced multi-output gaussian processes regression

The activated sludge process (ASP) is widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, the occurrence of filamentous sludge bulking often compromises the stable operation of the ASP. For timely diagnosis of filamentous sludge bulking for an activated sludge process in advance, this study proposed a Multi-Output Gaussian Processes Regression (MGPR) model for multi-step prediction and presented the Vector auto-regression (VAR) to learn the MGPR modelling deviation. The resulting models and associated uncertainty levels are used to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), such that the evolution of SVI can be predicted for both one-step and multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prognosis of filamentous bulking sludge with real-time SVI prediction were tested through a simulation study. The results demonstrated that the proposed methodology was capable of predicting future SVI with good accuracy, thereby providing sufficient time for filamentous sludge bulking.     

Dynamic modeling of filamentous bulking in lab-scale activated sludge processes

For years now, biological wastewater treatment plants rely on activated sludge systems in which a complex ecosystem, constituted mainly of bacteria and protozoa, (bio)degrade the incoming pollutants. Filamentous bulking, a phenomenon in which the filamentous organisms dominate the activated sludge is still a widespread problem in the operation of activated sludge processes with often severe economic and environmental consequences. Image analysis offers promising perspectives for early detection of filamentous bulking because the morphology parameters of the activated sludge respond rather fast to changing process conditions. This paper aims at exploiting this information in black box models to predict the evolution of the sludge volume index (SVI), a laboratory measurement currently exploited to quantify the sludge settleability. More specifically, dynamic ARX models are investigated as a function of organic loading and digital image analysis information (such as the total filament length per image and some representative mean floc shape parameters). The model’s performances are compared on the basis of a squared errors like quality criterion. While the identification results are very promising, the validation of the models on other independently generated data sets, depends on which data set is used for identification. The best performing models have (a combination of) the total filament length, one of the floc elongation parameters and the fractal dimension as inputs.     

城市污水处理过程污泥膨胀识别与抑制综述

污泥膨胀是活性污泥法污水处理过程中常见的一类异常工况, 且具有严重危害性, 研究污泥膨胀的识别和抑制方法对城市污水处理过程正常运行意义重大. 本文主要针对城市污水处理过程中污泥膨胀的识别和抑制方法进行综述. 首先, 文章概述了城市污水处理过程, 介绍了污泥膨胀的概念、主要特点、类型和成因; 其次, 概述了基于微生物生理特征、机理模型、图像识别和数据驱动的污泥膨胀识别方法, 分析其发展现状并指出优缺点; 然后, 概述了基于过程调控和机理特征的污泥膨胀抑制方法, 分析其发展现状并对比优缺点; 最后, 总结全文, 指出了城市污水处理过程污泥膨胀识别和抑制面临的主要问题, 并对其研究趋势进行了展望.     

Risk assessment modelling of microbiology-related solids separation problems in activated sludge systems

This paper proposes a risk assessment model for settling problems of microbiological origin in activated sludge systems (filamentous bulking, foaming and rising sludge). The aim of the model is not to diagnose microbiology-related solids separation problems with absolute certainty but to quantify in dynamic scenarios whether simulated operational procedures and control strategies lead to favourable conditions for them to arise or not. The rationale behind the model (which integrates the mechanisms of standard activated sludge models with empirical knowledge), its implementation in a fuzzy rule-based system and the details of its operation are illustrated in the different sections of the paper. The performance of the risk assessment model is illustrated by evaluating a number of control strategies facing different short-term influent conditions as well as long-term variability using the IWA/COST simulation benchmark. The results demonstrate that some control strategies, although performing better regarding operating costs and effluent quality, induce a higher risk for solids separation problems. In view of these results, it is suggested to integrate empirical knowledge into mechanistic models to increase reliability and to allow assessment of potential side-effects when simulating complex processes.     

Prediction of activated sludge bulking based on a self-organizing RBF neural network

Despite extensive research that has been done on sludge bulking, it remains a widespread problem in the operation of activated sludge processes, which brings severe economic and environmental consequences. In this study, a self-organizing radial basis function (SORBF) neural network method is utilized to predict the evolution of the sludge volume index (SVI). The hidden nodes in the SORBF neural network can be grown or pruned based on the node activity (NA) and mutual information (MI) to achieve the appropriate network complexity and maintain overall computational efficiency. The growing and pruning criteria of the SORBF can vary its structure dynamically with the objective to enhance its performance. Moreover, the input–output selection to calculate the SVI values is also discussed. The variables with key relations to the sludge bulking are used as the inputs for the SVI. Finally, the SORBF neural network is applied to the activated sludge wastewater treatment processes (WWTPs) for predicting the SVI, and then for predicting the sludge bulking. Experimental results show the excellent performance of the SORBF method. The performance comparison demonstrates the effectiveness of the proposed SORBF.     

Fault detection of sludge bulking using a self-organizing type-2 fuzzy-neural-network

Fault detection is important in the operation of wastewater treatment process (WWTP). In this paper, to ensure the process safety and effluent qualities, an intelligent fault detection (IFD) method, based on self-organizing type-2 fuzzy-neural-network (SOT2FNN) and intelligent identification method, was developed to detect and identify different types of sludge bulking. The main advantages of IFD are as follows. First, a data-driven framework, based on a data-driven model and an intelligent identification algorithm, was developed to facilitate the fault diagnosis. Second, a SOT2FNN, based on the intensity of information transmission algorithm and adaptive second-order algorithm, was designed to predict the sludge volume index (SVI) with high accuracy to provide necessary information for process monitoring. Third, an intelligent identification method, using the target-related identification algorithm (TRIA), was proposed to extract the correlation information to identify the types of sludge bulking. Finally, simulations and experimental examples were provided to confirm the effectiveness of the proposed IFD method.     

Effects of wearing aircrew protective clothing on physiological and cognitive responses under various ambient conditions

Heat stress can be a significant problem for pilots wearing protective clothing during flights, because they provide extra insulation which prevents evaporative heat loss. Heat stress can influence human cognitive activity, which might be critical in the flying situation, requiring efficient and error-free performance. This study investigated the effect of wearing protective clothing under various ambient conditions on physiological and cognitive performance. On several occasions, eight subjects were exposed for 3 h to three different environmental conditions; 0 degrees C at 80% RH, 23 degrees C at 63% RH and 40 degrees C at 19% RH. The subjects were equipped with thermistors, dressed as they normally do for flights (including helmet, two layers of underwear and an uninsulated survival suit). During three separate exposures the subjects carried out two cognitive performance tests (Vigilance test and DG test). Performance was scored as correct, incorrect, missed reaction and reaction time. Skin temperature, deep body temperature, heart rate, oxygen consumption, temperature and humidity inside the clothing, sweat loss, subjective sensation of temperature and thermal comfort were measured. Rises in rectal temperature, skin temperature, heart rate and body water loss indicated a high level of heat stress in the 40 degrees C ambient temperature condition in comparison with 0 degrees C and 23 degrees C. Performance of the DG test was unaffected by ambient temperature. However, the number of incorrect reactions in the Vigilance test was significantly higher at 40 degrees C than at 23 degrees C (p = 0.006) or 0 degrees C (p = 0.03). The effect on Vigilance performance correlated with changes in deep-body temperature, and this is in accordance with earlier studies that have demonstrated that cognitive performance is virtually unaffected unless environmental conditions are sufficient to change deep body temperature.     

阴极开放式质子交换膜燃料电池实验性研究

本文围绕实验室自制的开放式阴极自增湿型质子交换膜燃料电池开展了大量相关实验,采用FLUKETi25红外温度成像仪测得了各种操作条件下电池表面温度分布图像。实验结果表明：在封闭式阳极（anodedead—end）操作条件下,液态水会在阳极逐渐积累而影响反应气的传质,造成电池输出性能的衰减。通过阳极排气可以使电池性能恢复。纵观电堆表面温度分布情况,总体呈现出沿氢气流道方向递增的趋势,且随着电流密度的增大,这种温度分布的不均匀性变得更加明显。在实验所测试的范围内,电堆的平均输出功率密度达到了583mW／cm2。     

基于特征提取的中药水提液膜分离预测系统

为了找出中药水提液膜过程中影响膜污染的主要因素和预测膜污染的程度以防止膜污染,提出了应用遗传神经网络提取影响中药水提液膜分离过程的特征因素的方法,并以特征因素为输入向量,使用神经网络、支持向量机等建立预测模型,开发并实现了集成化的综合分析和预测系统.介绍了中药水提液膜分离预测系统的体系结构、主要功能、运行情况及开发的关键技术.实验结果表明,该集成化的综合分析系统较单一分析建模预测精度更高.     

Effects of wearing aircrew protective clothing on physiological and cognitive responses under various ambient conditions

Heat stress can be a significant problem for pilots wearing protective clothing during flights, because they provide extra insulation which prevents evaporative heat loss. Heat stress can influence human cognitive activity, which might be critical in the flying situation, requiring efficient and error-free performance. This study investigated the effect of wearing protective clothing under various ambient conditions on physiological and cognitive performance. On several occasions, eight subjects were exposed for 3 h to three different environmental conditions; 0°C at 80% RH, 23°C at 63% RH and 40°C at 19% RH. The subjects were equipped with thermistors, dressed as they normally do for flights (including helmet, two layers of underwear and an uninsulated survival suit). During three separate exposures the subjects carried out two cognitive performance tests (Vigilance test and DG test). Performance was scored as correct, incorrect, missed reaction and reaction time. Skin temperature, deep body temperature, heart rate, oxygen consumption, temperature and humidity inside the clothing, sweat loss, subjective sensation of temperature and thermal comfort were measured. Rises in rectal temperature, skin temperature, heart rate and body water loss indicated a high level of heat stress in the 40°C ambient temperature condition in comparison with 0°C and 23°C. Performance of the DG test was unaffected by ambient temperature. However, the number of incorrect reactions in the Vigilance test was significantly higher at 40°C than at 23°C (p = 0.006) or 0°C (p = 0.03). The effect on Vigilance performance correlated with changes in deep-body temperature, and this is in accordance with earlier studies that have demonstrated that cognitive performance is virtually unaffected unless environmental conditions are sufficient to change deep body temperature.