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Your search keyword '"Jiang, Guoming"' showing total 24 results
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24 results on '"Jiang, Guoming"'

7. Artificial intelligence-based adaptive anomaly detection technology for IaaS cloud virtual machines.

Author

Jiang, Guoming

Subjects
ARTIFICIAL intelligence, ANOMALY detection (Computer security), HYPERVISOR (Computer software), MACHINE learning
Abstract

As infrastructure-as-a-service clouds quickly grow, an increasing number of businesses and people are moving their application development to the cloud. The purpose of the research is to solve the problem of identifying memory anomalies in cloud virtual machines and improve the accuracy of the model in detecting abnormal situations. This paper presents a model for detecting virtual machine anomalies in IaaS cloud platform. The model considers the unique properties of monitoring metrics as time-series data and proposes an approach based on four important virtual machine monitoring metrics. The study also develops an adaptive anomaly detection system based on deep Q-network algorithms and migration learning principles for the variety of VM monitoring data in the cloud. The testing findings reveal that utilizing a Zoom layer with a 2-kernel size can increase detection accuracy to 96.7%. This demonstrates that a portion of the experimental data can extract the temporal features using the Zoom layer and different kernel sizes. The research model for anomaly detection had a classification accuracy of 99.8%. The deep Q-network model's final anomaly detection accuracy varies from 96.7 to 98.6%. The outcomes of the research improved the system's security and dependability, showed the worth of the overall framework design, and significantly decreased the number of resources needed for system operation and maintenance. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Improved Towed Airborne Three-Axis Magnetic Gradient Anomalies Navigation

Author

Xu, Supeng, Jin, Zixiang, Jiang, Guoming, Zhang, Guibin, Liang, Jian, Sun, Wei, Zhang, Lianzhi, Fan, Zhenyu, and Dong, Genwang

Abstract

Precise geomagnetic navigation of aircraft without GPS can be challenging. However, the low accuracy of traditional magnetic data and the limitations of the existing geomagnetic matching algorithms affect its implementation. We propose a set of strategies to address these issues. First, we designed a towed airborne three-axis magnetic gradient device for measurements, which effectively reduced the influence of geomagnetic diurnal variation, geomagnetic storms, and aircraft interference. Second, we introduced a least-squares correction algorithm to enhance the accuracy of the magnetic data by reducing the heading error resulting from the magnetometer sensor. The algorithm improved the internal coincidence total accuracy (ICTA) from 0.6529 to 0.0186 nT, a remarkable 35-fold increase. Finally, we established a geomagnetic matching navigation algorithm called aeromagnetic gradient iterative closest contour point (AGICCP) for a three-axis gradient field, which corrected the cumulative error of the inertial navigation system (INS) in long-distance navigation. Our simulation experiments demonstrated that AGICCP outperforms the traditional ICCP and vector ICCP (VICCP) algorithms, reducing the position error from 736.96 and 174.70 m, respectively, to 26.73 m. The suggested magnetic gradient device and the proposed algorithms significantly improve the accuracy of geomagnetic navigation. This study provides new insights into precise airborne navigation with great practical significance.

Published
2024
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