Low temperature densification by lithium co-doping and its effect on ionic conductivity of samarium doped ceria electrolyte

Low temperature densification and improving the ionic conductivity of doped ceria electrolyte is important for the realization of efficient intermediate temperature solid oxide fuel cell system. Herein, we report the effect of lithium co-doping (1, 3, 5 and 7?mol%) in 20?mol% samarium doped ceria on the low temperature sinterability and conductivity. The synthesized nanoparticles by citrate-nitrate combustion method showed a decrease in lattice parameter and increase in oxygen vacancy with lithium content after calcination due to the substitution of Li⁺ into CeO₂ lattice. Upon sintering at 900?°C, the density improved and reached a maximum value of 98.6% for 5% Li which exhibited a dense microstructure than at 7% Li. 5%Li co-doping exhibited the best conductivity of 3.65?×?10^?04–1.81?×?10^?3 S?cm^?1 in the operative temperature range of IT-SOFC (550–700?°C).Our results demonstrate the significance of lithium as co-dopant for efficient low temperature sintering as well as improving the electrolyte conductivity.     

Ionic transport properties and their variations with grain size in nanostructured double doped cerias such as Ce0.8Gd0.1Pr0.1O2−δ and Ce0.8Gd0.15Pr0.05O2−δ

This work presents the ionic transport properties in some nanocrystalline double doped cerias, i.e., Ce_0.8Gd_0.1Pr_0.1O_2−δ and Ce_0.8Gd_0.15Pr_0.05O_2−δ with various average grain sizes, in the intermediate temperature region. The correlations between electrical and dielectric properties of these materials have been established and variation of conductivity with respect to temperature has been thoroughly discussed. All the materials are found to be ionic in nature and show high value of ionic conductivity at intermediate temperatures. The nanocrystalline Ce_0.8Gd_0.1Pr_0.1O_2−δ material (irrespective of grain size), shows lowest association energy, i.e., 0.03 eV, which is close to the theoretically predicted lowest value (0.02 eV) in double doped ceria. A repulsive force is expected between the free oxygen vacancies at the grain boundary regions at higher temperatures, which restricts the rise in grain boundary conductivity and results in decrease in total conductivity.     

一种基于背景声音识别的移动社会活动推荐系统

随着智能手机和移动互联网的快速发展,人们的生活方式正在发生着变革。目前,智能手机终端集成了不同种类的传感器,如GPS、Wi-Fi、摄像头、麦克风等。通过内置传感器,可以搜集用户的相关信息,利用这些信息能有效地理解和识别用户行为,并为用户提供更好的个性化服务。文中主要面向大型工作或生活社区(如大学校园),提出基于背景声音识别的社会活动推荐系统MSSAR(Mobile Sound Sensing and Activity Recommender)。该系统通过音频传感器采集用户的背景声音,实时感知用户所处环境,并对用户实时活动(如在咖啡厅、在开会等)进行识别。MSSAR还能结合用户在线交互历史数据计算其偏好及社会关系亲密度,并据此为用户进行活动推荐。本系统基于以人为本的计算理念,对增强用户间社会联系、促进社区成员间活动交流具有积极意义。     

基于排序的关联分类算法

提出了一种基于排序的关联分类算法.利用基于规则的分类方法中择优方法偏爱高精度规则的思想和考虑尽可能多的规则,改进了CBA(Classification Based on Associations)只根据少数几条覆盖训练集的规则构造分类器的片面性.首先采用关联规则挖掘算法产生后件为类标号的关联规则,然后根据长度、置信度、支持度和提升度等对规则进行排序,并在排序时删除对分类结果没有影响的规则.排序后的规则加上一个默认分类便构成最终的分类器.选用20个UCI公共数据集的实验结果表明,提出的算法比CBA具有更高的平均分类精度.     

两类典型多目标跟踪算法的性能分析与比较

在处理目标跟踪的两类主要方法中,一类是通过数据关联来解决,如PDA和JPDA等;另一类则是绕过关联直接处理,如随机集、GM-PHD等。该文从两类典型方法中各选取一种有代表性的方法,如JPDA与GM-PHD,首先通过分析两种算法主要步骤的计算量,得到相应算法总计算量的解析表达式;然后根据观测与目标状态之间关联复杂程度,分3种情况对两类算法的计算量进行比较;最后以仿真说明算法的跟踪效果,并以算法运行时间来验证计算量公式的正确性。     

基于WNN和EGA的信息安全风险评估

为克服传统信息安全风险评估模型在人为权重分配中的主观性,提出一种基于小波神经网络(WNN)和熵权-灰色关联(EGA)的信息安全风险定量评估模型。该模型利用WNN得到风险事件的风险值以及各个实体的风险统计状况,通过EGA得到该风险状况排序,并计算平均风险水平,从而有效评估信息系统的安全风险。     

2006 Award Winners: APA/APAGS Award for Distinguished Graduate Student in Professional Psychology.

The APA/APAGS Award for Distinguished Graduate Student in Professional Psychology is awarded on an annual basis by the Board of Professional Affairs (BPA) and the American Psychological Association of Graduate Students (APAGS) to a graduate student who has demonstrated outstanding practice and application of psychology. A qualified candidate must demonstrate exemplary performance in working with an underserved population in an applied setting or have developed an innovative method for delivering health services to an underserved population. This document lists the winners of this award from 2002 to 2006, and provides the citation, brief profile, and selected bibliography for the 2006 recipient, Mona M. Amer. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A novel manufacturing defect detection method using association rule mining techniques

In recent years, manufacturing processes have become more and more complex, and meeting high-yield target expectations and quickly identifying root-cause machinesets, the most likely sources of defective products, also become essential issues. In this paper, we first define the root-cause machineset identification problem of analyzing correlations between combinations of machines and the defective products. We then propose the Root-cause Machine Identifier (RMI) method using the technique of association rule mining to solve the problem efficiently and effectively. The experimental results of real datasets show that the actual root-cause machinesets are almost ranked in the top 10 by the proposed RMI method.