SYJan 12, 2015
A Novel Clustering Approach Based on Group Quasi-Consensus of Unstable Dynamic Linear High-Order Multi-Agent SystemsNing Cai, Chen Diao, M. Junaid Khan
This paper introduces a novel approach of clustering, which is based on group consensus of dynamic linear high-order multi-agent systems. The graph topology is associated with a selected multi-agent system, with each agent corresponding to one vertex. In order to reveal the cluster structure, the agents belonging to a similar cluster are expected to aggregate together. As theoretical foundation, a necessary and sufficient condition is given to check the group consensus. Two numerical instances are shown to illustrate the process of approach.
SPMar 5, 2018
Data fusion of multivariate time series: Application to noisy 12-lead ECG signalsChen Diao, Bin Wang
12-lead ECG signals fusion is crucial for further ECG signal processing. In this paper, a novel fusion data algorithm is proposed. In the method, 12-lead ECG signals are appropriately converted to a single-lead physiological signal via the idea of the local weighted linear prediction algorithm. For effectively inheriting the quality characteristics of the 12-lead ECG signals, the fuzzy inference system is rationally designed to estimate the weighted coefficient in our algorithm. Experimental results indicate that the algorithm can obtain desirable results on synthetic ECG signals, noisy ECG signals and realistic ECG signals.