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| 1 | Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond显示文摘自上世纪60年代作为现代估计开山之作的卡尔曼滤波器(Kalman filter)的诞生,时间序列状态空间模型应用于各类动态估计问题吸引了大量的研究关注。特别是,寻求实现闭环马尔科夫-贝叶斯递归(比如,从一个高斯先验到一个高斯后验,本文称之为高斯共轭)的解析解成为一般时间序列滤波器设计的主流思路。其面临的主要挑战包括:系统的非线性、多模态(包括机动模型)、复杂不确定性(比如未知的系统输入,非高斯噪声等)和系统约束(包括循环随机变量)等。这些挑战不断触生新的理论、算法与滤波技术,以实现所期望的参数共轭递归。本文对最新研究进行分类、系统回顾,强调了一些容易被忽略的要点。着重介绍了高精观测非线性系统、高斯后验和机动多模态、以及复杂未知系统输入与约束,以弥补当前文献介绍的不足。同时,本文提出一些新的思考:一是一阶马尔科夫转移模型的替代模型,二是有关计算复杂度的滤波器评价。 | Tian-cheng LI Jin-ya SU Wei LIU Juan M.CORCHADO | 2017 | Frontiers of Information Technology & Electronic Engineering2017，18，12： | 8 |
| 2 | Multi-EAP: Extended EAP for multi-estimate extraction for SMC-PHD filter显示文摘The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction(MEE), is an essential requirement for a multi-target filter, whose key performance assessments are based on accuracy, computational efficiency and reliability. The probability hypothesis density(PHD) filter, implemented by the sequential Monte Carlo approach,affords a computationally efficient solution to general multi-target filtering for a time-varying number of targets, but leaves no clue for optimal MEE. In this paper, new data association techniques are proposed to distinguish real measurements of targets from clutter, as well as to associate particles with measurements. The MEE problem is then formulated as a family of parallel singleestimate extraction problems, facilitating the use of the classic expected a posteriori(EAP) estimator, namely the multi-EAP(MEAP) estimator. The resulting MEAP estimator is free of iterative clustering computation, computes quickly and yields accurate and reliable estimates. Typical simulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of faster processing speed and better estimation accuracy. | Li Tiancheng Juan M.Corchado Sun Shudong Fan Hongqi | 2017 | Chinese Journal of Aeronautics2017，30，1： | 4 |
| 3 | 粒子滤波重采样:同分布原则、一种新方法以及综合对比(英文)显示文摘目的:重采样方法是粒子滤波设计的重要环节,也是避免或克服'权值退化'和'多样性匮乏'这一对粒子滤波难点问题的关键。当前研究领域已有几十余种重采样方法,然而尚缺乏一个基础性的重采样设计原则以及对这些方法的综合性能对比。针对于此,本文提出重采样'同分布'设计原则,并在此基础上,提出一种能够最大程度满足同分布原则的最优重采样方法。本文希望所提出的重采样同分布原则以及新方法有利于进一步的新方法设计或已有方法的工程选用。创新点:理论上严格定义了同分布原则作为重采样方法设计的普遍性原则,给出三种同分布测度方法;提出了一种最小采样方差(MSV:minimum sampling variance)最优重采样方法,在满足渐近无偏性的前提下获得最小采样方差。方法:给出三种'重采样同分布'测度方法:Kullback-Leibler偏差,Kolmogorov-Smirnov统计和采样方差(sampling variance)。所提出的最小采样方差重采样放宽了无偏性条件,仅满足渐近无偏,但获得了最小采样方差(参见定理2-4论证以及仿真性能对比)。结论:重采样前后粒子的概率分布应该统计上一致(即'同分布')是重采样方法设计的一个重要原则。明确这一基本原则有利于规范化重采样新方法的设计与工程选用。所提出的MSV重采样新方法渐近无偏,并具有最小采样方差的优异理论特性,即最优地满足同分布原则。算法性能分析表明:大多数无偏或者渐近无偏重采样方法在滤波精度上差异较小,但是在采样方差、计算效率方面差异较大。另一方面,基于一些特殊规则或者问题模型设计的重采样方法可能具有特别优势。 | Tian-cheng LI Gabriel VILLARRUBIA Shu-dong SUN Juan M.CORCHADO Javier BAJO | 2015 | Frontiers of Information Technology & Electronic Engineering2015，16，11： | 3 |
| 4 | Collaborative learning via social computing显示文摘Educational innovation is a field that has been greatly enriched by using technology in its processes, resulting in a learning model where information comes from numerous sources and collaboration takes place among multiple students. One attractive challenge within educational innovation is the design of collaborative learning activities from the social computing point of view, where collaboration is not limited to student-to-student relationships, but includes student-to-machine interactions. At the same time, there is a great lack of tools that give support to the whole learning process and are not restricted to specific aspects of the educational task. In this paper, we present and evaluate context-aware framework for collaborative learning applications(CAFCLA) as a solution to these problems. CAFCLA is a flexible framework that covers the entire process of developing collaborative learning activities, taking advantage of contextual information and social interactions. Its application in the experimental case study of a collaborative WebQuest within a museum has shown that, among other benefits, the use of social computing improves the learning process, fosters collaboration, enhances relationships, and increases engagement. | Ricardo S.ALONSO Javier PRIETO óscar GARCíA Juan M.CORCHADO | 2019 | Frontiers of Information Technology & Electronic Engineering2019，20，2： | 1 |
| 5 | Editorial:Special issue on distributed computing and artificial intelligence显示文摘4:1!Google’s artificial intelligence(AI)program,Alpha Go,has won Go Master Lee Sedol in a best-of-five competition held in Korean March 9-15,2016.Seen by many as a landmark moment for AI,the outcome did not come as a surprise,considering the excellent combination of 1920 CPUs with so- | Juan M.CORCHADO Li WEIGANG Javier BAJO Fei WU Tian-cheng LI | 2016 | Frontiers of Information Technology & Electronic Engineering2016，17，4： | 0 |
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