With the continuous development of the Internet,e-commerce and popularization of financial payment means,traditional constraints have become unfavorable factors in our country's economic development.Meanwhile,the ...With the continuous development of the Internet,e-commerce and popularization of financial payment means,traditional constraints have become unfavorable factors in our country's economic development.Meanwhile,the rapid development of mobile communications provides solid basis for the development of mobile payment business.In this case,the mobile payment industry naturally will become an industry to deal with all walks of life and promote the development of logistics,manufacturing,service industries,public information and all kinds of innovative mobile technology.Then a branch of financial payment will naturally be formed.As mobile technology continuously improves cost savings and the efficiency of payment,the development of the mobile payment industry began to be analyzed.In this thesis,for study Chinese mobile payment,a detailed analysis of the development status will be investigated,the problems of development of China's mobile payment will be revealed and the strategic development of the final payment will be put forward.They all have profound significance.展开更多
为了更有效地进行图像去噪,提出了一种基于分块奇异值分解(Singular value decomposition,SVD)的两级图像去噪方法,该方法首先将含噪图像中具有相似结构的图像块组织成具有很强相关性的图像块组;然后,利用二维奇异值分解去除图像块组中...为了更有效地进行图像去噪,提出了一种基于分块奇异值分解(Singular value decomposition,SVD)的两级图像去噪方法,该方法首先将含噪图像中具有相似结构的图像块组织成具有很强相关性的图像块组;然后,利用二维奇异值分解去除图像块组中每个相似块的内部相关性,利用一维奇异值分解去除相似图像块组之间的冗余;最后,通过硬阈值方法收缩变换系数实现图像与噪声的有效分离.为了进一步提高去噪效果,对含噪图像再次进行上述操作.不同的是,在第二级去噪过程中,相似图像块组根据第一级估计出的图像计算获得且相似图像块间的相关性通过离散余弦变换去除.仿真实验表明,提出的两级图像去噪算法不仅可以较大程度地去除图像噪声,还能有效保留图像细节,取得了良好的去噪效果.展开更多
异质出行群体的合理划分是提升出行需求预测准确性和实施主动式交通管理的关键,针对目前城市多方式出行群体划分研究的不足,在分析出行习惯与偏好差异影响因素的基础上,提出基于潜在类分析(Latent Class Cluster Analysis,LCCA)的城市...异质出行群体的合理划分是提升出行需求预测准确性和实施主动式交通管理的关键,针对目前城市多方式出行群体划分研究的不足,在分析出行习惯与偏好差异影响因素的基础上,提出基于潜在类分析(Latent Class Cluster Analysis,LCCA)的城市异质出行群体识别方法.以北京市为例,应用揭示性偏好调查进行基础数据收集,运用Mplus软件编程实现LCCA模型估计.模型将出行者划分为三类异质出行群体,群体1:低出行+方式均衡组(20.4%),群体2:中高出行+小汽车偏好组(30.3%),群体3:高出行+绿色交通组(49.3%).模型回归结果表明:群体2、3的百分比与北京市小汽车、公共交通出行比例之差均不超过2%,证明提出的出行群体识别方法有效,个人属性、出行者对各交通方式的认知与态度对群体隶属影响显著.针对各异质出行群体提出了相应的绿色交通发展措施,为城市交管部门的精细化出行管控提供重要依据.展开更多
文摘With the continuous development of the Internet,e-commerce and popularization of financial payment means,traditional constraints have become unfavorable factors in our country's economic development.Meanwhile,the rapid development of mobile communications provides solid basis for the development of mobile payment business.In this case,the mobile payment industry naturally will become an industry to deal with all walks of life and promote the development of logistics,manufacturing,service industries,public information and all kinds of innovative mobile technology.Then a branch of financial payment will naturally be formed.As mobile technology continuously improves cost savings and the efficiency of payment,the development of the mobile payment industry began to be analyzed.In this thesis,for study Chinese mobile payment,a detailed analysis of the development status will be investigated,the problems of development of China's mobile payment will be revealed and the strategic development of the final payment will be put forward.They all have profound significance.
文摘为了更有效地进行图像去噪,提出了一种基于分块奇异值分解(Singular value decomposition,SVD)的两级图像去噪方法,该方法首先将含噪图像中具有相似结构的图像块组织成具有很强相关性的图像块组;然后,利用二维奇异值分解去除图像块组中每个相似块的内部相关性,利用一维奇异值分解去除相似图像块组之间的冗余;最后,通过硬阈值方法收缩变换系数实现图像与噪声的有效分离.为了进一步提高去噪效果,对含噪图像再次进行上述操作.不同的是,在第二级去噪过程中,相似图像块组根据第一级估计出的图像计算获得且相似图像块间的相关性通过离散余弦变换去除.仿真实验表明,提出的两级图像去噪算法不仅可以较大程度地去除图像噪声,还能有效保留图像细节,取得了良好的去噪效果.
文摘异质出行群体的合理划分是提升出行需求预测准确性和实施主动式交通管理的关键,针对目前城市多方式出行群体划分研究的不足,在分析出行习惯与偏好差异影响因素的基础上,提出基于潜在类分析(Latent Class Cluster Analysis,LCCA)的城市异质出行群体识别方法.以北京市为例,应用揭示性偏好调查进行基础数据收集,运用Mplus软件编程实现LCCA模型估计.模型将出行者划分为三类异质出行群体,群体1:低出行+方式均衡组(20.4%),群体2:中高出行+小汽车偏好组(30.3%),群体3:高出行+绿色交通组(49.3%).模型回归结果表明:群体2、3的百分比与北京市小汽车、公共交通出行比例之差均不超过2%,证明提出的出行群体识别方法有效,个人属性、出行者对各交通方式的认知与态度对群体隶属影响显著.针对各异质出行群体提出了相应的绿色交通发展措施,为城市交管部门的精细化出行管控提供重要依据.