Model of Markov Chains in Space-Time Random Environments

A general framework of stochastic model for a Markov chain in a space-time random environment is introduced, here the environment ξ^＊：={ξ1,x∈N,x∈ X}is a random field. We study the dependence relations between the environment and the original chain, especially the ＂feedback＂. Some equivalence theorems and law of large numbers are obtained.     

高校艺术设计教育模式现状研究与思考

在市场经济大潮和教育改革形势的推动下，高校艺术设计教青迈向了更具时代特色的全面转型期。但也出现了结构性失衡的问题，培养的学生难以适应社会的要求。为了顺应经济发展的需要，高校应创建一种基于能力培养的以市场需求为核心的人才培养机制。     

基于蔡氏电路和压缩感知的图像压缩加密方法

针对当前信号采集传输高效性和安全性的实际需求, 提出了基于蔡氏电路和压缩感知的图像压缩加密方法。首先利用SHA-256算法生成原始信号的消息摘要; 其次利用摘要生成蔡氏电路的初始值; 再次利用蔡氏电路生成的混沌模拟信号经过8位量化采集和数字移位差分生成伪随机序列, 从而构建出混沌观测矩阵并对信号进行压缩采样; 最后将压缩后的信号通过生成的伪随机序列进行置乱加密和灰度加密, 生成压缩加密后的信号。实验结果表明, 该方法生成的混沌观测矩阵性能优于传统的随机观测矩阵, 同时能够有效提高信号的传输效率及安全性。     

基于错误特征的MLC闪存最小和译码算法

由于多级单元(multi level cell,MLC)闪存存储信道中随机电报噪声(random telegraph noise,RTN)、〖JP〗数据保持噪声(data retention noise,DRN)和单元间干扰(cell-to-cell interference,CCI)严重影响了MLC闪存阈值电压,从而导致获取的对数似然比(log-likelihood ratio,LLR)不够准确而影响了软判决译码时MLC闪存的低密度校验(low-density parity-check codes,LDPC)码的性能。在深入分析MLC闪存错误特征的基础上,通过利用MLC阈值电压的熵函数计算相邻MLC阈值电压分布的重叠区域来确定存储比特的可靠度,设计了MLC存储比特LLR值的动态更新策略。从而,提出了RTN、DRN和CCI噪声模型下适用于MLC闪存的LDPC码改进的最小和译码算法。仿真结果表明,与传统的LDPC码最小和译码算法相比较,MLC闪存信道下所改进的MLC闪存的LDPC码最小和译码算法具有更好的译码性能与更少的平均迭代次数。     

对弈熵率在藏棋“杰布杰曾”上的应用

藏族民间有多种对棋博弈,如＂尼格尔＂、＂密芒＂、＂杰布杰曾＂等等,此类游戏的胜负可能性可以通过熵率来计算。通过对弈双方比较,即哪方熵率高,哪方自由度就高的关系,可以算出取胜的可能性高低。文章计算的双门＂杰布杰曾＂对弈双方熵率为：国王=3.9419,大臣=3.7045,由此可得出国王取胜可能性较高的结论。     

NA随机变量序列的Berry-Esseen界

负相关NA（ negatively associated）随机变量序列是一类较弱的相依序列。讨论了NA随机变量序列的Berry-Esseen界问题，在不同的条件下，得到了Berry-Esseen界分别为O （ n-1/4· log n · log log n）和 O （ n-1/6· log n · log log n），结果推广了已有文献的相关结论。     

Ion transport-related resistive switching in film sandwich structures

Resistive switching memories based on ion transport and related electrochemical reactions have been extensively studied for years. To utilize the resistive switching memories for high-performance information storage applications, a thorough understanding of the key information of ion transport process, including the mobile ion species, the ion transport paths, as well as the electrochemical reaction behaviors of these ions should be provided for material and device optimization. Moreover, ion transport is usually accompanied by processes of microstructure modification, phase transition, and energy band structure variation that lead to further modulation of other physical properties, e.g., magnetism, optical emission/absorbance, etc., in the resistive switching materials. Therefore, novel resistive switching memories that are controlled through additional means of magnetic or optical stimulus, or demonstrate extra functionalities beyond information storage, can be made possible via well-defined ion transportation in various switching materials and devices. In this contribution, the mechanism of ion transport and related resistive switching phenomena in thin film sandwich structures is discussed first, followed by a glanceat the recent progress in the development of high-performance and multifunctional resistive switching memories. A brief perspective of the ion transport-based resistive switching memories is addressed at the end of this review.     

基于随机模糊样本的统计学习理论基础

首先介绍了随机模糊理论的基本内容，进而证明了基于随机模糊样本的统计学习理论的关键定理，且讨论了学习过程一致收敛速度的界，为系统建立不确定样本的统计学习理论并构建相应的支持向量机奠定了理论基础．     

Robust function-on-function regression model with nonparametric random effects

Extended t-process is robust to outliers and inherits many attractive properties from the Gaussian process. In this paper, we provide a function-on-function nonparametric random-effects model using extended t-process priors in which we consider heterogeneity of individual effect, flexible mean function, nonparametric covariance function and robustness. A likelihood-based estimation procedure is constructed to estimate parameters involved in the model. Information consistency for the parameter estimation is provided. Simulation studies and a real data example are further investigated to evaluate the performance of the developed procedures.     

基于集成学习的信号控制交叉口排队长度估计

基于电子警察(LPR)数据和网联车辆轨迹数据，提出了一种基于集成学习的信号控制交叉口排队长度估计方法。通过分析不同数据条件下估计方法的适用条件和精度水平，运用随机森林方法设计集成学习器，并构建电子警察和网联车辆轨迹感知信息及不同方法估计结果和真实排队长度之间的非线性映射关系。仿真结果表明：本方法的平均绝对误差为1.3 m·周期^-1·车道^-1，平均绝对百分比误差为1.4%。