Hollow multi-shelled structure(HoMS)is the novel multifunctional structural system,which are con-structed with nanoparticles as structural units,featuring two or more shells,multiple interfaces,and numerous chan-nels ...Hollow multi-shelled structure(HoMS)is the novel multifunctional structural system,which are con-structed with nanoparticles as structural units,featuring two or more shells,multiple interfaces,and numerous chan-nels and demonstrating outstanding properties in energy conversion and mass transfer.In recent years,owing to the breakthroughs in synthetic methods,the diversity of composition and structure of HoMS has been greatly enriched,showing broad application prospects in energy,catalysis,environment and other fields.This review focuses on the research status of HoMS for catalytic applications.Firstly,the new synthesis method for HoMS,namely the sequen-tial templating approach,is introduced from both practical and theoretical perspectives.Then,it summarizes and discusses the structure-performance relationship between the shell structure and catalytic performance.The unique temporal-spatial ordering property of mass transport in HoMS and the major breakthroughs it brings in catalytic applications are discussed.Finally,it looks forward to the opportunities and challenges in the development of HoMS.展开更多
针对姿态多变化的飞机自动目标识别中的低识别率问题,提出了一种基于DSm T(Dezert-Smarandache theory)与隐马尔可夫模型(Hidden Markov model,HMM)的飞机多特征序列信息融合识别算法(Multiple features and sequential information fus...针对姿态多变化的飞机自动目标识别中的低识别率问题,提出了一种基于DSm T(Dezert-Smarandache theory)与隐马尔可夫模型(Hidden Markov model,HMM)的飞机多特征序列信息融合识别算法(Multiple features and sequential information fusion,MFSIF).其创新性在于将单幅图像的多特征信息融合识别和序列图像信息融合识别进行有机结合.首先,对图像进行二值化预处理,并提取目标的Hu矩和轮廓局部奇异值特征;然后,利用概率神经网络(Probabilistic neural networks,PNN)构造基本信度赋值(Basic belief assignment,BBA);接着,利用DSm T对该图像的不同特征进行融合,从而获得HMM的观察值序列;再接着,利用隐马尔可夫模型对飞机序列信息融合,计算观察值序列与各隐马尔可夫模型之间的相似度,从而实现姿态多变化的飞机目标自动识别;最后,通过仿真实验,验证了该算法在飞机姿态发生较大变化时,依然可以获得较高的正确识别率,同时在实时性方面也可以满足飞机目标识别的要求.另外,在飞机序列发生连续遮挡帧数τ≤6的情况下,也具有较高的飞机目标正确识别率.展开更多
基金Supported by National Natural Science Foundation of China （60874063） and Innovation and Scientific Research Foundation of Graduate Student of Heilongjiang Province （YJSCX2012-263HLJ）
文摘Hollow multi-shelled structure(HoMS)is the novel multifunctional structural system,which are con-structed with nanoparticles as structural units,featuring two or more shells,multiple interfaces,and numerous chan-nels and demonstrating outstanding properties in energy conversion and mass transfer.In recent years,owing to the breakthroughs in synthetic methods,the diversity of composition and structure of HoMS has been greatly enriched,showing broad application prospects in energy,catalysis,environment and other fields.This review focuses on the research status of HoMS for catalytic applications.Firstly,the new synthesis method for HoMS,namely the sequen-tial templating approach,is introduced from both practical and theoretical perspectives.Then,it summarizes and discusses the structure-performance relationship between the shell structure and catalytic performance.The unique temporal-spatial ordering property of mass transport in HoMS and the major breakthroughs it brings in catalytic applications are discussed.Finally,it looks forward to the opportunities and challenges in the development of HoMS.
文摘针对姿态多变化的飞机自动目标识别中的低识别率问题,提出了一种基于DSm T(Dezert-Smarandache theory)与隐马尔可夫模型(Hidden Markov model,HMM)的飞机多特征序列信息融合识别算法(Multiple features and sequential information fusion,MFSIF).其创新性在于将单幅图像的多特征信息融合识别和序列图像信息融合识别进行有机结合.首先,对图像进行二值化预处理,并提取目标的Hu矩和轮廓局部奇异值特征;然后,利用概率神经网络(Probabilistic neural networks,PNN)构造基本信度赋值(Basic belief assignment,BBA);接着,利用DSm T对该图像的不同特征进行融合,从而获得HMM的观察值序列;再接着,利用隐马尔可夫模型对飞机序列信息融合,计算观察值序列与各隐马尔可夫模型之间的相似度,从而实现姿态多变化的飞机目标自动识别;最后,通过仿真实验,验证了该算法在飞机姿态发生较大变化时,依然可以获得较高的正确识别率,同时在实时性方面也可以满足飞机目标识别的要求.另外,在飞机序列发生连续遮挡帧数τ≤6的情况下,也具有较高的飞机目标正确识别率.
