We give the optimal I-(VI-)total colorings of mC_(4)which are vertex-distinguished by multiple sets by the use of the method of constructing a matrix whose entries are the suitable multiple sets or empty sets and the ...We give the optimal I-(VI-)total colorings of mC_(4)which are vertex-distinguished by multiple sets by the use of the method of constructing a matrix whose entries are the suitable multiple sets or empty sets and the method of distributing color set in advance.Thereby we obtain I-(VI-)total chromatic numbers of mC_(4)which are vertex-distinguished by multiple sets.展开更多
Let G be a simple graph with no isolated edge. An Ⅰ-total coloring of a graph G is a mapping φ : V(G) ∪ E(G) → {1, 2, · · ·, k} such that no adjacent vertices receive the same color and no adjacent ...Let G be a simple graph with no isolated edge. An Ⅰ-total coloring of a graph G is a mapping φ : V(G) ∪ E(G) → {1, 2, · · ·, k} such that no adjacent vertices receive the same color and no adjacent edges receive the same color. An Ⅰ-total coloring of a graph G is said to be adjacent vertex distinguishing if for any pair of adjacent vertices u and v of G, we have C_φ(u) = C_φ(v), where C_φ(u) denotes the set of colors of u and its incident edges. The minimum number of colors required for an adjacent vertex distinguishing Ⅰ-total coloring of G is called the adjacent vertex distinguishing Ⅰ-total chromatic number, denoted by χ_at^i(G).In this paper, we characterize the adjacent vertex distinguishing Ⅰ-total chromatic number of outerplanar graphs.展开更多
在说话人识别研究中,基于身份认证矢量(identity vector,i-vector)的子空间建模被证明是目前最前沿最有效的说话人建模技术,其中如何有效准确地估计总体变化子空间矩阵T成为影响系统性能好坏的关键问题.本文针对i-vector技术如何在新的...在说话人识别研究中,基于身份认证矢量(identity vector,i-vector)的子空间建模被证明是目前最前沿最有效的说话人建模技术,其中如何有效准确地估计总体变化子空间矩阵T成为影响系统性能好坏的关键问题.本文针对i-vector技术如何在新的应用环境下进行总体变化子空间矩阵T的自适应估计问题进行了研究,并提出了两种行之有效的自适应估计算法.在由美国国家标准技术局(American National Institute of Standard and Technology,NIST)组织的2008年说话人识别核心评测数据库以及自行采集的测试数据库上的实验结果显示,不论采用测试集数据本身还是与测试集较匹配的开发集数据,通过本文所提的自适应算法来更新总体变化子空间矩阵均可以使更新后的子空间更有利于新测试数据下的低维子空间描述,在新的测试环境下都更有利于说话人分类.此外实验结果还表明基于多子空间拼接的子空间自适应方法性能明显优于迭代自适应方法,而且两者的结合可达到最优的识别性能,且此时利用开发集数据进行自适应可以接近其利用测试集数据进行自适应得到的最优性能.展开更多
基金Supported by the National Natural Science Foundation of China(11761064)
文摘We give the optimal I-(VI-)total colorings of mC_(4)which are vertex-distinguished by multiple sets by the use of the method of constructing a matrix whose entries are the suitable multiple sets or empty sets and the method of distributing color set in advance.Thereby we obtain I-(VI-)total chromatic numbers of mC_(4)which are vertex-distinguished by multiple sets.
基金Supported by the National Natural Science Foundation of China(61163037,61163054,61363060)
文摘Let G be a simple graph with no isolated edge. An Ⅰ-total coloring of a graph G is a mapping φ : V(G) ∪ E(G) → {1, 2, · · ·, k} such that no adjacent vertices receive the same color and no adjacent edges receive the same color. An Ⅰ-total coloring of a graph G is said to be adjacent vertex distinguishing if for any pair of adjacent vertices u and v of G, we have C_φ(u) = C_φ(v), where C_φ(u) denotes the set of colors of u and its incident edges. The minimum number of colors required for an adjacent vertex distinguishing Ⅰ-total coloring of G is called the adjacent vertex distinguishing Ⅰ-total chromatic number, denoted by χ_at^i(G).In this paper, we characterize the adjacent vertex distinguishing Ⅰ-total chromatic number of outerplanar graphs.
文摘在说话人识别研究中,基于身份认证矢量(identity vector,i-vector)的子空间建模被证明是目前最前沿最有效的说话人建模技术,其中如何有效准确地估计总体变化子空间矩阵T成为影响系统性能好坏的关键问题.本文针对i-vector技术如何在新的应用环境下进行总体变化子空间矩阵T的自适应估计问题进行了研究,并提出了两种行之有效的自适应估计算法.在由美国国家标准技术局(American National Institute of Standard and Technology,NIST)组织的2008年说话人识别核心评测数据库以及自行采集的测试数据库上的实验结果显示,不论采用测试集数据本身还是与测试集较匹配的开发集数据,通过本文所提的自适应算法来更新总体变化子空间矩阵均可以使更新后的子空间更有利于新测试数据下的低维子空间描述,在新的测试环境下都更有利于说话人分类.此外实验结果还表明基于多子空间拼接的子空间自适应方法性能明显优于迭代自适应方法,而且两者的结合可达到最优的识别性能,且此时利用开发集数据进行自适应可以接近其利用测试集数据进行自适应得到的最优性能.
