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Guanrong Chen

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6 papers
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6

EAAI Journal 2023 Journal Article

A prospect theory-based MABAC algorithm with novel similarity measures and interactional operations for picture fuzzy sets and its applications

  • Tao Wang
  • Xinxing Wu
  • Harish Garg
  • Qian Liu
  • Guanrong Chen

Picture fuzzy set (PFS) is one the reliable tool to handle the uncertainties in the data as compared to the intuitionistic fuzzy set (IFS) or fuzzy set. PFS simultaneously handle the four degrees namely, membership, neutrality, non-membership, and refusal, and thus widely applicable to solve the real-life decision-making problems more accurately. Keeping their advantages, in this paper, we present some interactive operational laws for the picture fuzzy numbers (PFNs) to aggregate picture fuzzy information. Also, we state some new information measures namely picture fuzzy similarity measures (PFSimMs) based on fuzzy strict negations, which can overcome the various drawbacks of the existing PFSimMs. The various properties and their features are studied in detail to show their advantages. Finally, we develop a prospect theory-based multi-attributive border approximation area comparison (MABAC) method under picture fuzzy environment by using the proposed operational laws and PFSimMs to solve the decision-making problems. The applicability of the developed algorithm is explained through a numerical example and show its superiorities.

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JBHI Journal 2023 Journal Article

MEGA: Machine Learning-Enhanced Graph Analytics for Infodemic Risk Management

  • Ching Nam Hang
  • Pei-Duo Yu
  • Siya Chen
  • Chee Wei Tan
  • Guanrong Chen

The COVID-19 pandemic brought not only global devastation but also an unprecedented infodemic of false or misleading information that spread rapidly through online social networks. Network analysis plays a crucial role in the science of fact-checking by modeling and learning the risk of infodemics through statistical processes and computation on mega-sized graphs. This article proposes MEGA, M achine Learning- E nhanced G raph A nalytics, a framework that combines feature engineering and graph neural networks to enhance the efficiency of learning performance involving massive graphs. Infodemic risk analysis is a unique application of the MEGA framework, which involves detecting spambots by counting triangle motifs and identifying influential spreaders by computing the distance centrality. The MEGA framework is evaluated using the COVID-19 pandemic Twitter dataset, demonstrating superior computational efficiency and classification accuracy.

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JBHI Journal 2021 Journal Article

Attention-Based Parallel Multiscale Convolutional Neural Network for Visual Evoked Potentials EEG Classification

  • Zhongke Gao
  • Xinlin Sun
  • Mingxu Liu
  • Weidong Dang
  • Chao Ma
  • Guanrong Chen

Electroencephalography (EEG) decoding is an important part of Visual Evoked Potentials-based Brain-Computer Interfaces (BCIs), which directly determines the performance of BCIs. However, long-time attention to repetitive visual stimuli could cause physical and psychological fatigue, resulting in weaker reliable response and stronger noise interference, which exacerbates the difficulty of Visual Evoked Potentials EEG decoding. In this state, subjects' attention could not be concentrated enough and the frequency response of their brains becomes less reliable. To solve these problems, we propose an attention-based parallel multiscale convolutional neural network (AMS-CNN). Specifically, the AMS-CNN first extract robust temporal representations via two parallel convolutional layers with small and large temporal filters respectively. Then, we employ two sequential convolution blocks for spatial fusion and temporal fusion to extract advanced feature representations. Further, we use attention mechanism to weight the features at different moments according to the output-related interest. Finally, we employ a full connected layer with softmax activation function for classification. Two fatigue datasets collected from our lab are implemented to validate the superior classification performance of the proposed method compared to the state-of-the-art methods. Analysis reveals the competitiveness of multiscale convolution and attention mechanism. These results suggest that the proposed framework is a promising solution to improving the decoding performance of Visual Evoked Potential BCIs.

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TCS Journal 2019 Journal Article

Invulnerability of planar two-tree networks

  • Yuzhi Xiao
  • Haixing Zhao
  • Yaping Mao
  • Guanrong Chen

Let us model the network by an undirected graph G, in which each edge fails independently with probability q ∈ [ 0, 1 ], the all-terminal reliability of the network is the probability that the graph G remains connected and it is one of the important measure for the invulnerability of the network. Clearly, the all-terminal reliability can be written as a polynomial in either q. Theoretically, it has been proved that calculating all-terminal reliability for planar networks is #P-complete. Up now we cannot find any results for some special classes of the planar networks. This paper studies the all-terminal reliability of the two-tree networks and its generalizations. A linear algorithm for calculating the reliability of a planar two-tree networks is proposed, with algorithmic complexity O ( n ), where n is the number of steps. Then both the uniformly-most reliable network model and the uniformly-worst reliable network model are determined by using the algorithm. Finally, a polynomial algorithm for planar two-connected networks is proposed based on an expended algorithm and the corresponding uniformly-most reliable network model and uniformly-worst reliable network model are also presented, with computational complexity O ( n ), where n is the number of steps.

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ICRA Conference 1997 Conference Paper

A fuzzy PD controller for multi-link robot control: stability analysis

  • Ya-Chen Hsu
  • Guanrong Chen
  • Edgar N. Sánchez

A new multi-input multi-output (MIMO) fuzzy proportional-derivative (PD) controller is designed and analyzed in this paper for its asymptotic stability when used for multi-link robot arm systems. Simple sufficient conditions for designing stable control gains are derived via the Lyapunov method.

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