闭式外腔循环太阳能墙集热效果实验研究

文章设计了闭式外腔循环太阳能墙系统,从理论和实验两方面研究太阳能墙传热传质规律。以大连市冬季为研究背景,基于对实验测温点不同时刻的持续监测,从逐时曲线中分析了不同环境下闭式外腔循环系统内部温度以及室内获得热量后室温的变化情况。结果表明,太阳能墙集热效率与太阳辐射强度密切相关,在晴朗天气下,白天部分时段单一太阳能墙就能满足采暖要求,不满足采暖要求时可以选择空气源热泵辅助采暖。     

Kevlar织物软壁包容环抗冲击数值仿真分析研究

针对机匣包容性问题,提出Kevlar平纹织物本构模型的拟合方法,分别采用单层壳、多层壳、层合壳三种有限元建模方式对Kevlar织物软壁包容环弹道冲击过程进行模拟,发展了机匣包容性仿真分析模型。结果表明,多层壳模型和层合壳模型的计算结果更准确,并且可以通过调节摩擦因子使仿真结果与试验结果更接近。在此基础上研究了撞击点位置和弹体入射姿态对弹体剩余速度的影响。结果表明:在较小偏移距离内,撞击点的位置对剩余速度的影响可以忽略不计;滚转角和俯仰角对剩余速度影响较小,而对于偏航角,当角度大于30°时,剩余速度会随着角度的增大明显呈下降趋势,且弹体出射姿态也会发生明显变化。     

钓鱼网站检测研究现状与发展趋势的计量分析

从包含相关SCI、CPCI-S与BKCI-S等数据库的Web of Science核心合集数据库出发，对数据库中存在的近13年的国内外文献使用Cite Space与Carrot2分析工具进行聚类分析。力求系统客观地得到国际上钓鱼网站检测技术研究领域的关注热点、研究脉络、著名机构以及核心刊物等研究现状。筛选出具有代表性的文章，采取少量顶级文章精读、部分知名文章粗读的方式对目前钓鱼网站检测技术所涉及到的方法做整理与归类。根据上文的分析对钓鱼网站检测技术的发展方向做了规律性展望，以求对此方向下相关研究人员的研究提供一定的参考。     

面向智能客服系统的情感分析技术

该文以阿里小蜜为例,对智能客服系统中的情感分析技术进行比较全面的介绍,包括情感分析算法模型的原理及其在智能客服系统的多个应用场景中的实际落地使用方式和效果分析。智能客服在解决客户高频业务问题的同时,也需要给客户提供多维度的、具有类人能力的助理、导购、语聊和娱乐等服务,提高客户对智能客服机器人的整体满意度。在此过程中,情感分析技术在机器人类人能力建设中起到了至关重要的作用。该文围绕智能客服系统中人机结合的服务形式,从六个维度总结和介绍了情感分析技术在智能客服系统中的应用场景,包括用户情感检测、用户情感安抚、情感生成式语聊、客服服务质检、会话满意度预估和智能人工入口。     

基于“动态学习数据分析”的智慧课堂 评价考核体系构建

This paper firstly describes the current situation and limitations of learning evaluation and assessment in colleges and uni- versities; then, it analyzes the possibility of implementing the learning evaluation and assessment based on "dynamic learning data analysis"; then, it describes the design process of the learning evaluation and assessment system in detail; finally, it determines the various assessment items and carries out quantitative processing to better apply them to actual teaching.     

无线电综合测试仪校准装置的研究及其不确定度分析

This calibration device of radio comprehensive tester is studied and established in this paper. It solves the verification and calibration of a large number of general-purpose radio comprehensive testers. The calibration device consists of four calibration sub- systems and calibration software system, including RF generator, audio generator, audio analyzer and RF analyzer. It also researches on the configuration of standard equipment and analyzes the uncertainty of the calibration devices, ensuring accurate and reliable val- ue transfer.     

An interval observer for uncertain continuous‐time linear systems

To design robust interval observers for uncertain continuous‐time linear systems, a new set‐integration approach is proposed to compute trajectory tubes for the estimation error. Because this approach, the order‐preserving condition on the dynamics of the estimation error is no longer required. Therefore, synthesis methods can be used to compute observer gains that reduce the impact of the system uncertainties on the accuracy of the estimated state enclosures. The performance of the proposed approach is showcased through illustrative numerical examples.     

Development of a statistical model to classify driving stress levels using galvanic skin responses

Many studies have demonstrated the strong relationships between physiological responses and driving stress, but they have done little to build a model that could be used to identify a driver's stress accurately in real time. The objective of this study is to develop a model that accurately classifies driving stress by monitoring physiological responses—specifically galvanic skin response (GSR). GSR data were collected from nine drivers with licenses obtained in the US in real road driving situations with two stress conditions—rest period (low stress) and highway or city driving (high stress). The validation drive was performed by one driver with licenses obtained in South Korea in real long‐term road driving situations with two stress conditions—rural area (low stress) and highway or highway under construction (high stress). Those two conditions were used to build a binary logistic regression model to classify low stress or high stress based on a driver's measured hand GSR. The overall classification accuracy of the developed model was found to be 85.3%, and the accuracy of cross validation, with a testing dataset, was found to be 83.2%. A simple logit model was developed to identify drivers' stress by incorporating their GSR data. The developed model can be embedded in a wearable device equipped with GSR sensors for drivers to detect their stress level in real time.