SWShiying Wang

Shiying Wang

Shanxi Normal University
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Shiying Wang has a total of 54 co-authors. Here are the top co-authors by number of shared publications:

Latest Publications

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    Edge Bipancyclicity of Wheel Networks

    by Suyalabateer, Wei Feng, Shiying Wang, Chunlei Xu, Xueqiong Chen

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    Fault-Tolerant Maximal Local-Connectivity on Leaf-Sort Graphs

    by Lina Zhao, Shiying Wang, Hongmei Li

    An interconnection network is usually modeled as a graph, in which vertices and edges correspond to processors and communication links, respectively. Connectivity is an important index to measure the fault-tolerant capability of interconnection networks. A graph [ ] is called to be maximally local-connected if each pair of vertices [ ] and [ ] of [ ] are connected by min[ ] vertex-disjoint paths. A graph [ ] is [ ]-fault-tolerant maximally local-connected if [ ] remains maximally local-connected for any [ ] with [ ]. In this paper, we show that the [ ]-dimensional leaf-sort graph [ ] is [ ]-fault-tolerant maximally local-connected for [ ]; under the restricted condition that each vertex has at least two fault-free adjacent vertices, [ ] is [ ]-conditional fault-tolerant maximally local-connected for odd [ ] and [ ]-conditional fault-tolerant maximally local-connected for even [ ]. Furthermore, we show that [ ] is [ ]-fault-tolerant one-to-many maximally local-connected for [ ].

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    Edge-Fault-Tolerant Edge-Bipancyclicity of Modified Bubble-Sort Graphs

    by Shiying Wang, Huiying Zhang, Lina Zhao, Shangwei Lin

    The modified bubble-sort network is a famous Cayley graph model, which has strong symmetry and recursive structure, as well as good topological structure and properties. The cycle embedding problem is widely discussed in many networks, and edge fault tolerance is an important issue for networks since edge failures may occur when a network is put into use. In this paper, we prove that for any edge subset F of the n-dimensional modified bubble-sort graph [ ], every edge of [ ] lies on a cycle of every even length from [ ] to [ ] when [ ] does not exceed [ ]. As a corollary, the cycles of every even length from 4 to [ ] can be embedded into the n-dimensional modified bubble-sort graph when the number of fault edges in the graph does not exceed [ ].

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    Fault-tolerant strong Menger connectivity with conditional faults on wheel networks

    by Shiying Wang, Lina Zhao, Su Wang

    Cited by 1
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    Conditional Edge Fault Hamiltonicity of Exchanged Hypercube

    by Chen Wang, Shiying Wang

    The exchanged hypercube is regarded as an attractive alternative to the hypercube. In this paper, we investigate the Hamiltonicity of the exchanged hypercube [ ] [ ]. We further show the conditional edge fault Hamiltonicity of the exchanged hypercube.

    Cited by 1
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    Open Access

    Reliability Analysis of (n, k)-Bubble-Sort Networks Based on Extra Conditional Fault

    by Lina Zhao, Shiying Wang, Feng Dou

    Given a graph G=(V(G),V(E)), a non-negative integer g and a set of faulty vertices F⊆V(G), the g-extra connectivity of G, denoted by κg(G), is the smallest cardinality of F, whose value of deletion, if exists, will disconnect G and give each remaining component at least g+1 vertices. The g-extra diagnosability of the graph G, denoted by tg(G), is the maximum cardinality of the set F of fault vertices that the graph can guarantee to identify under the condition that each fault-free component has more than g vertices. In this paper, we determine that g-extra connectivity of (n,k)-bubble-sort network Bn,k is κg(Bn,k)=n+g(k−2)−1 for 4≤k≤n−1 and 0≤g≤n−k. Afterwards, we show that g-extra diagnosability of Bn,k under the PMC model (4≤k≤n−1 and 0≤g≤n−k) and MM* model (4≤k≤n−1 and 0≤g≤min{n−k−1,k−2}) is tg(Bn,k)=n+g(k−1)−1, respectively.

    Cited by 5
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    One-to-one Disjoint-path Covers of Leaf-sort Graphs

    by Shiying Wang, Huanhuan Wang, Lina Zhao, Wei Feng

    Parallel paths of interconnection networks have attracted much attention in parallel computing systems, they are studied in terms of disjoint paths in an undirected graph [ ]. A [ ]-disjoint path cover (k-DPC for short) of a graph [ ] is a set of [ ] (internally) disjoint paths that altogether cover every vertice of the graph. A bipartite graph [ ] is one-to-one bi-k-DPC if there is a k-DPC between any pair of vertices in different partite sets. A bipartite graph is hamiltonian laceable if there exists a hamiltonian path between any pair of vertices in different partite sets. The [ ]-dimensional leaf-sort graph [ ] has many good properties, including bipartite, symmetry, vertex-transitive. In this paper, we prove that [ ] has one-to-one bi-[ ]-DPC between any two vertices [ ] and [ ] in [ ], where [ ], [ ] in different partite sets, and [ ] is hamiltonian laceable.

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    Open Access

    The m-Component Connectivity of Leaf-Sort Graphs

    by Shiying Wang, Hongmei Li, Lina Zhao

    Connectivity plays an important role in measuring the fault tolerance of interconnection networks. As a special class of connectivity, m-component connectivity is a natural generalization of the traditional connectivity of graphs defined in terms of the minimum vertex cut. Moreover, it is a more advanced metric to assess the fault tolerance of a graph G. Let G=(V(G),E(G)) be a non-complete graph. A subset F(F⊆V(G)) is called an m-component cut of G, if G−F is disconnected and has at least m components (m≥2). The m-component connectivity of G, denoted by cκm(G), is the cardinality of the minimum m-component cut. Let CFn denote the n-dimensional leaf-sort graph. Since many structures do not exist in leaf-sort graphs, many of their properties have not been studied. In this paper, we show that cκ3(CFn)=3n−6 (n is odd) and cκ3(CFn)=3n−7 (n is even) for n≥3; cκ4(CFn)=9n−212 (n is odd) and cκ4(CFn)=9n−242 (n is even) for n≥4.

    Cited by 4
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    The Component Connectivity of Leaf-Sort Graphs

    by Shiying Wang, Hongmei Li, Lina Zhao

    In large-scale parallel computing and communication systems, an interconnect network structure is usually modeled as a graph, in which vertices and edges correspond to processors and communication links, respectively. In a graph, the vertices and edges are likely to fail, so we must think about the fault tolerance of a graph. Connectivity is an important parameter in the study of faulty tolerance for a graph. In this paper, we study a special class of connectivity: $m$-component connectivity, this is a natural generalization of the classical connectivity of graphs defined in terms of the minimum vertex-cut. Let $F$ is a vertex set of $G$ $(i.e, F\subseteq V(G))$, if the following conditions are satisfied, we say $F$ is a $m$-component cut: $(1)$ $G-F$ is disconnected; $(2)$ the number of components in $G-F$ is greater than or equal to $m$. In another word, the $m$-component connectivity $c\kappa_{m}(G)$ is defined as $min\{|F|~|~F\subseteq V(G)$ and $F$ is a $m$-component cut$\}$. Determining $m$-component connectivity is still unsolved in most interconnection networks even for small $m'$s. Leaf-sort graph is a special Cayley graph, it has many special properties that are different from other Cayley graphs. So we need to pay attention to some of it's special properties when we study it. In this paper, we can get the values: $c\kappa_{3}(CF_{n})=3n-6$ $(n$ is odd$)$ and $c\kappa_{3}(CF_{n})=3n-7$ $(n$ is even$)$ for $n\geq3$; $c\kappa_{4}(CF_{n})=\frac{9n-21}{2}$ $(n$ is odd$)$ and $c\kappa_{4}(CF_{n})=\frac{9n-24}{2}$ $(n$ is even$)$ for $n\geq4$; $c\kappa_{5}(CF_{n})=6n-16$ $(n$ is odd$)$ and $c\kappa_{5}(CF_{n})=6n-18$ $(n$ is even$)$ for $n\geq5$.

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    HrreNet: semantic segmentation network for moderate and high-resolution satellite images

    by Yanping Li, Lu Wang, Lei Zhang, Haiwen Chen, Shiying Wang, Xiaoying Wang

    Semantic segmentation is a commonly used intelligent interpretation method. However, semantic segmentation via deep learning usually faces several problems: resolution loss due to downsampling, difficulty in obtaining context information, and imprecise object boundaries. These problems lead to a poor performance when using satellite images instead of conventional images and limit the value of intelligent interpretation of satellite images in practical application. In this study, we construct a Qilian Ecological Resource Extraction Dataset (QERED) with multiple scene satellite images of Qilian after preprocessing via correction, fusion, mosaic, and annotation. We then propose a segmentation network, namely, High-resolution Resource Extracting Network (HrreNet), by using high-resolution feature representation, multi-scale context fusion, boundary refinement with relearning, and structural similarity loss. Experiments on QERED show that HrreNet greatly improves performance on small-size objects and slightly improves performance on larger-size objects. HrreNet achieves the best result of 73.36% mIoU on a moderate-resolution satellite dataset CCFD and 80.87% mIoU on a high-resolution satellite dataset Vaihingen.

    Cited by 4
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