从专业信息的网上检索看互联网信息检索的不足

文章结合互联网的特点 ,从几个方面较详细地介绍了专业信息的网上检索方法 ,并就检索过程中可能会遇到的难点提供了一些具体的技术解决措施。然后 ,针对目前互联网信息检索的实际使用状况 ,作者指出了几个有待改进的不足之处 ,并提出了一些具体的解决办法     

情感计算在网络远程教育系统中的应用：功能、研究现状及关键问题

情感能力是人类智能的重要标志,情感的缺失会影响网络远程教育的教学质量和学习者的学习效果。情感计算是和谐人机交互与人工智能领域中新的研究方向。在网络远程教育系统中应用情感计算理论与技术,可以进一步优化网络远程教育的功能,帮助教师监测远程学习者的情感变化,调整教学策略和方法,实时给予学习者情感反馈,使教学质量达到最佳。目前国内外情感计算在远程教育方面的应用研究还处于起步与探索阶段,所构建的远程教学系统对远程教学中师生情感生理特征与心理特征、多模情感信息融合机制及网络虚拟人机情感交互特性等考虑不足,系统原型在情感合成与表达、智能人机情感交互实现上存在着很大的难度。因此,构建与人类情感系统相吻合的、自然和谐的、人性化和智能化的网络远程教育系统,需要有效解决师生面部表情、语音情感特征信号的准确识别与提取,师生多模情感信息融合机制与和谐人机情感交互技术等关键问题。     

以微博为载体的高校思想政治教育探析

微博的出现迎合了大学生对社会新信息和新知识的渴求,它本身具备思想政治教育载体的身份特征以及思想政治教育的功能,使之在高校思想政治教育中的应用成为可能。鉴于微博对思想政治教育的双重影响,高校应从加强大学生的媒介素养教育、建立思想政治教育实践意义的高校微博平台及促进微博与高校思想政治理论课教学的结合三方面强化以微博为载体的高校思想政治教育。     

Location extraction from tweets

Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. These events are represented by three main dimensions: time, location and entity-related information. The focus of this paper is location, which is an essential dimension for geo-spatial applications, either when helping rescue operations during a disaster or when used for contextual recommendations. While the first type of application needs high recall, the second is more precision-oriented. This paper studies the recall/precision trade-off, combining different methods to extract locations. In the context of short posts, applying tools that have been developed for natural language is not sufficient given the nature of tweets which are generally too short to be linguistically correct. Also bearing in mind the high number of posts that need to be handled, we hypothesize that predicting whether a post contains a location or not could make the location extractors more focused and thus more effective. We introduce a model to predict whether a tweet contains a location or not and show that location prediction is a useful pre-processing step for location extraction. We define a number of new tweet features and we conduct an intensive evaluation. Our findings are that (1) combining existing location extraction tools is effective for precision-oriented or recall-oriented results, (2) enriching tweet representation is effective for predicting whether a tweet contains a location or not, (3) words appearing in a geography gazetteer and the occurrence of a preposition just before a proper noun are the two most important features for predicting the occurrence of a location in tweets, and (4) the accuracy of location extraction improves when it is possible to predict that there is a location in a tweet.     

Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities

Energy efficiency of public sector is an important issue in the context of smart cities due to the fact that buildings are the largest energy consumers, especially public buildings such as educational, health, government and other public institutions that have a large usage frequency. However, recent developments of machine learning within Big Data environment have not been exploited enough in this domain. This paper aims to answer the question of how to incorporate Big Data platform and machine learning into an intelligent system for managing energy efficiency of public sector as a substantial part of the smart city concept. Deep neural networks, Rpart regression tree and Random forest with variable reduction procedures were used to create prediction models of specific energy consumption of Croatian public sector buildings. The most accurate model was produced by Random forest method, and a comparison of important predictors extracted by all three methods has been conducted. The models could be implemented in the suggested intelligent system named MERIDA which integrates Big Data collection and predictive models of energy consumption for each energy source in public buildings, and enables their synergy into a managing platform for improving energy efficiency of the public sector within Big Data environment. The paper also discusses technological requirements for developing such a platform that could be used by public administration to plan reconstruction measures of public buildings, to reduce energy consumption and cost, as well as to connect such smart public buildings as part of smart cities. Such digital transformation of energy management can increase energy efficiency of public administration, its higher quality of service and healthier environment.     

声目标识别技术研究

本文对声目标识别的关键技术进行研究,探讨用小波分析来进行声信号处理和特征提取,用BP神经网络和支持向量机的模式分类方法来进行分类识别.通过对四种声信号的仿真实验表明,使用小波变换和人工神经网络技术及支持向量机分类技术相结合对声目标进行识别是有效、可行的.     

基于RSViewSE的港口粮流系统操作员站的设计

文章以某粮食港口为例,根据该项目的实际情况,介绍了该粮食港口的工艺流程并应用组态软件RSViewSE对该港口粮流系统操作员站进行了设计。人机界面简洁友好,操作使用方便,体现了RSView SE在实际应用中的灵活性以及卓越特性。     

VRML技术及应用

虚拟现实技术是一门涉及众多学科的新兴技术,在许多领域具有广泛的应用前景。概括介绍了VRML发展、定义及特点,最后介绍了VRML在机械设计方面的应用。     

Particle Swarm Optimization based predictive control of Proton Exchange Membrane Fuel Cell （PEMFC）

INTRODUCTION Fuel cells have attracted more attention in the last few years due to scarcity of the world energy source. The Proton Exchange Membrane Fuel Cell (PEMFC) is the focus of current development efforts because it is capable of higher power density and faster start-up than other fuel cells (Zhang et al., 2004). Research emphasis is on high power density with adequate energy conversion efficiency. PEMFC performance is related to many factors, among which electrolyte membrane …