全聚焦算法依靠信号的幅度信息进行延迟叠加(delay and sum,DAS)成像,实际应用中信号并非总能满足相干叠加这一前提,而非相干信号的叠加导致噪声和伪影。文章提出一种循环相干因子(circular coherence factor,CCF)加权的延迟乘和(delay ...全聚焦算法依靠信号的幅度信息进行延迟叠加(delay and sum,DAS)成像,实际应用中信号并非总能满足相干叠加这一前提,而非相干信号的叠加导致噪声和伪影。文章提出一种循环相干因子(circular coherence factor,CCF)加权的延迟乘和(delay multiply and sum,DMAS)CCF-DMAS优化算法,实现薄板中缺陷的兰姆波全聚焦成像。该方法考虑接收阵元间的空间相干性,对接收信号进行相乘耦合,利用数据中的相位信息计算相干因子实现自适应加权,以扩大相干和非相干信号间的差异,从而达到缩窄主瓣,减少旁瓣,提高成像分辨率的效果。建立超声阵列发射、接收实验系统,通过楔块耦合,在含通孔缺陷的锆合金薄板上激发S_(0)模态兰姆波,捕获全矩阵数据;通过CCF-DMAS算法对采集的数据相位加权,生成新的频率分量;利用带通滤波保留二次谐波分量进行全聚焦成像。实验结果表明:与DAS和DMAS全聚焦成像算法相比,CCF-DMAS全聚焦优化算法能够有效抑制噪声和伪影,信噪比提高约39%和22%,阵列性能指数提高约86%和69%,为薄板无损检测的后处理提供了一种有效的改进方案。展开更多
Given a simple graph G=(V,E)and its(proper)total coloringϕwith elements of the set{1,2,⋯,k},let wϕ(v)denote the sum of the color of v and the colors of all edges incident with v.If for each edge uv∈E,wϕ(u)≠wϕ(v),we ...Given a simple graph G=(V,E)and its(proper)total coloringϕwith elements of the set{1,2,⋯,k},let wϕ(v)denote the sum of the color of v and the colors of all edges incident with v.If for each edge uv∈E,wϕ(u)≠wϕ(v),we callϕa neighbor sum distinguishing total coloring of G.Let L={Lx∣x∈V⋃E}be a set of lists of real numbers,each of size k.The neighbor sum distinguishing total choosability of G is the smallest k for which for any specified collection of such lists,there exists a neighbor sum distinguishing total coloring using colors from Lx for each x∈V⋃E,and we denote it by[Math Processing Error].The known results of neighbor sum distinguishing total choosability are mainly about planar graphs.In this paper,we focus on 1-planar graphs.A graph is 1-planar if it can be drawn on the plane so that each edge is crossed by at most one other edge.We prove that[Math Processing Error]for any 1-planar graph G withΔ≥15,whereΔis the maximum degree of G.展开更多
文摘全聚焦算法依靠信号的幅度信息进行延迟叠加(delay and sum,DAS)成像,实际应用中信号并非总能满足相干叠加这一前提,而非相干信号的叠加导致噪声和伪影。文章提出一种循环相干因子(circular coherence factor,CCF)加权的延迟乘和(delay multiply and sum,DMAS)CCF-DMAS优化算法,实现薄板中缺陷的兰姆波全聚焦成像。该方法考虑接收阵元间的空间相干性,对接收信号进行相乘耦合,利用数据中的相位信息计算相干因子实现自适应加权,以扩大相干和非相干信号间的差异,从而达到缩窄主瓣,减少旁瓣,提高成像分辨率的效果。建立超声阵列发射、接收实验系统,通过楔块耦合,在含通孔缺陷的锆合金薄板上激发S_(0)模态兰姆波,捕获全矩阵数据;通过CCF-DMAS算法对采集的数据相位加权,生成新的频率分量;利用带通滤波保留二次谐波分量进行全聚焦成像。实验结果表明:与DAS和DMAS全聚焦成像算法相比,CCF-DMAS全聚焦优化算法能够有效抑制噪声和伪影,信噪比提高约39%和22%,阵列性能指数提高约86%和69%,为薄板无损检测的后处理提供了一种有效的改进方案。
基金supported by Guangdong Basic and Applied Basic Research Foundation(Natural Science Foundation of Guangdong Province,China,Grant No.2025A1515011904)the National Natural Science Foundation of China(Nos.12101285,12171222)+4 种基金the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2016D01C006)Guangdong Philosophy and Social Sciences Planning Project(Grant No.GD22CXW01)Research Platforms and Projects of Colleges and Universities in Guangdong(Grant No.2022KTSCX071)Scientific Research Innovation Project of Lingnan Normal University(LT2401)Lingnan Normal University(No.LT2410).
文摘Given a simple graph G=(V,E)and its(proper)total coloringϕwith elements of the set{1,2,⋯,k},let wϕ(v)denote the sum of the color of v and the colors of all edges incident with v.If for each edge uv∈E,wϕ(u)≠wϕ(v),we callϕa neighbor sum distinguishing total coloring of G.Let L={Lx∣x∈V⋃E}be a set of lists of real numbers,each of size k.The neighbor sum distinguishing total choosability of G is the smallest k for which for any specified collection of such lists,there exists a neighbor sum distinguishing total coloring using colors from Lx for each x∈V⋃E,and we denote it by[Math Processing Error].The known results of neighbor sum distinguishing total choosability are mainly about planar graphs.In this paper,we focus on 1-planar graphs.A graph is 1-planar if it can be drawn on the plane so that each edge is crossed by at most one other edge.We prove that[Math Processing Error]for any 1-planar graph G withΔ≥15,whereΔis the maximum degree of G.
