Browsing by Author "Zhang, Chen"

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    Geomagnetic gradient-assisted evolutionary algorithm for long-range underwater navigation
    (IEEE, 2020-10-30) Zhang, Jiayu; Zhang, Tao; Shin, Hyosang; Wang, Jian; Zhang, Chen
    Extensive research results have shown that animals like pigeons and turtles can use geomagnetic information for long-distance migration and homing. This article studies the bionic navigation method inspired by magnetotaxis behavior without prior knowledge. The problem of bionic geomagnetic navigation is generalized as an autonomous search of navigation path under the excitation of geomagnetic environment. The geomagnetic gradient-assisted evolutionary algorithm for long-range underwater navigation is proposed. In order to optimize the navigation path, the heading angle predicted by the geomagnetic gradient is used to constrain the sample space in the evolutionary algorithm. Then, according to the principle of multiparameter simultaneous convergence, the evaluation function is improved to enhance the reliability and accuracy of the navigation path. Simulations of the algorithm before and after improvement are carried out based on the data retrieved from the enhanced magnetic model (EMM). The performance of the improved method is evaluated and verified in the case of the area with normal geomagnetic field (GF), geomagnetic anomaly area, and multiple destinations. The simulation results show that the search efficiency and the straightness of the navigation path are greatly improved. The reason is that the constraint of sample space reduces the randomness in the process of navigation path search, and the improved evaluation function can evaluate the quality of samples more accurately. The improved algorithm also has good performance in the geomagnetic anomaly area, which indicates the potential application in the future.
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    A novel industrial intrusion detection method based on threshold-optimized CNN-BiLSTM-attention using ROC curve
    (IEEE, 2020-09-09) Lan, Mindi; Luo, Jun; Chai, Senchun; Chai, Ruiqi; Zhang, Chen; Zhang, Baihai
    In recent years, many researchers have proposed many intrusion detection methods to protect the industrial network. However, there are two existing problems among them: one is that they only consider the overall accuracy rate (AC) while ignoring the problem of class imbalance; another one is that they have considered the problem of class imbalance, but the detection rate (DR) is low and false positive rate (FR) is high for minority classes. In order to improve AC and DR of minority classes, we propose a method called threshold-optimized CNN-BiLSTM-Attention that combines CNN-BiLSTM-Attention model, with threshold modification method based on receiver operating characteristic (ROC) curve. In this method, we use CNN-BiLSTM-Attention model as a classifier and modify threshold of the classifier through ROC curve. To evaluate the proposed method, we have performed experiments on the standard industrial data set. And the experimental results show that the proposed method can improve AC and the DR of minority classes at low FR, which is better than other intrusion detection methods.