A bipartite graph model for implicit feature extraction in Chinese reviews

Single Spectrum Bipartite Graph (SSBG) model is developed to forecast thunderstorms over Kolkata(22∘32′N,88∘20′E)during the premonsoon season (April-May). The statistical distribution of normal probability is observed for temperature, relative humidity, convective available potential energy (CAPE), and convective inhibition energy (CIN) to quantify the threshold values of the parameters for the prevalence of thunderstorms. Method of conditional probability is implemented to ascertain the possibilities of the occurrence of thunderstorms within the ranges of the threshold values. The single spectrum bipartite graph connectivity model developed in this study consists of two sets of vertices; one set includes two time vertices (00UTC, 12UTC) and the other includes four meteorological parameters: temperature, relative humidity, CAPE, and CIN. Three distinct ranges of maximal eigen values are obtained for the three categories of thunderstorms. Maximal eigenvalues for severe, ordinary, and no thunderstorm events are observed to be(2.6±0.12),(1.88±0.09), and(1.26±.03), respectively. The ranges of the threshold values obtained using ten year data (1997–2006) are considered as the reference range and the result is validated with the IMD (India Meteorological Department) observation, Doppler Weather Radar (DWR) Products, and satellite images of 2007. The result reveals that the model provides 12- to 6-hour forecast (nowcasting) of thunderstorms with 96% to 98% accuracy.

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Graph Model-Based Lane-Marking Feature Extraction for Lane Detection

Sensors ◽

10.3390/s21134428 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4428

Author(s):

Ju-Han Yoo ◽

Dong-Hwan Kim

Keyword(s):

Feature Extraction ◽

Environmental Changes ◽

Graph Model ◽

Local Maximum ◽

Input Image ◽

Lane Detection ◽

Connected Subgraph ◽

Feature Extraction Method ◽

Model Based ◽

Lane Marking

This paper presents a robust, efficient lane-marking feature extraction method using a graph model-based approach. To extract the features, the proposed hat filter with adaptive sizes is first applied to each row of an input image and local maximum values are extracted from the filter response. The features with the maximum values are fed as nodes to a connected graph structure, and the edges of the graph are constructed using the proposed neighbor searching method. Nodes related to lane-markings are then selected by finding a connected subgraph in the graph. The selected nodes are fitted to line segments as the proposed features of lane-markings. The experimental results show that the proposed method not only yields at least 2.2% better performance compared to the existing methods on the KIST dataset, which includes various types of sensing noise caused by environmental changes, but also improves at least 1.4% better than the previous methods on the Caltech dataset which has been widely used for the comparison of lane marking detection. Furthermore, the proposed lane marking detection runs with an average of 3.3 ms, which is fast enough for real-time applications.

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Robustness Analysis of Urban Transit Networks Based on Bipartite Graph Model

IFAC Proceedings Volumes ◽

10.3182/20090924-3-it-4005.00042 ◽

2009 ◽

Vol 42 (20) ◽

pp. 246-251 ◽

Cited By ~ 1

Author(s):

Houli DUAN ◽

Zhiheng LI ◽

Yi ZHANG ◽

Zuo ZHANG ◽

Danya YAO ◽

...

Keyword(s):

Bipartite Graph ◽

Robustness Analysis ◽

Graph Model ◽

Urban Transit ◽

Transit Networks

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Isoperimetric Numbers of Randomly Perturbed Intersection Graphs

Symmetry ◽

10.3390/sym11040452 ◽

2019 ◽

Vol 11 (4) ◽

pp. 452

Author(s):

Yilun Shang

Keyword(s):

Social Networks ◽

Social Interactions ◽

Bipartite Graph ◽

Scientific Collaboration ◽

Graph Model ◽

Intersection Graph ◽

Collaboration Network ◽

Intersection Graphs ◽

Mild Conditions ◽

Isoperimetric Number

Social networks describe social interactions between people, which are often modeled by intersection graphs. In this paper, we propose an intersection graph model that is induced by adding a sparse random bipartite graph to a given bipartite graph. Under some mild conditions, we show that the vertex–isoperimetric number and the edge–isoperimetric number of the randomly perturbed intersection graph on n vertices are Ω ( 1 / ln n ) asymptomatically almost surely. Numerical simulations for small graphs extracted from two real-world social networks, namely, the board interlocking network and the scientific collaboration network, were performed. It was revealed that the effect of increasing isoperimetric numbers (i.e., expansion properties) on randomly perturbed intersection graphs is presumably independent of the order of the network.

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