Mr. Junhua Huang Profile

Junhua Huang

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Proceedings Article | 18 March 2024 Paper

Evaluation method for communication equipment health status based on dynamic composite weights

X. Yang, J. Huang, Y. Liang, H. Zhang, P. Xie, X. Gao, Y. Wang, Y. Zhao, T. Bao, Y. Ning, J. Zhang

Proceedings Volume 13104, 131040P (2024) https://doi.org/10.1117/12.3021353

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To address the health status assessment of communication equipment under various uncertainty factors, a novel evaluation method for communication equipment health status has been developed, considering the significance of equipment metrics comprehensively. This framework integrates the subjective Analytic Hierarchy Process (AHP) and the objective Criteria Importance Through Intercriteria Correlation (CRITIC) method to determine the weighting of assessment metrics. Furthermore, a method for dynamically adjusting metric weights based on data volatility is proposed, thereby circumventing the issue of inaccurate assessments stemming from metric data fluctuations when encountering equipment instability. Empirical testing demonstrates the practical utility of this approach in accurately assessing the health status of communication equipment.

Proceedings Article | 4 April 2023 Paper

Multi-fault localization based on knowledge graph combined with network topology in optical networks

J. Huang, Z. Chen, B. Zhu, Y. Jing, Y. Liang, X. Yang, R. Li, J. Han, Y. Zhao

Proceedings Volume 12617, 126173Y (2023) https://doi.org/10.1117/12.2666156

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When faults occur in optical networks, many derivative alarms are generated from the root alarms, because of the derived properties of optical network alarms. In a large and complex optical network, the management system will receive tremendous alarms even though there is only one actual fault. Therefore, accurate fault localization has significance for network operation and maintenance. The complex relationship between alarms and faults makes it difficult to locate faults, especially multiple faults. To solve this problem, this paper proposes a fault localization method based on knowledge graph and network topology information. Using the information association ability of the knowledge graph, the implicit relationship between the alarm data is mined, and the graph neural network is used to perform automatic fault reasoning on the alarm knowledge graph. Further, the network topology information is integrated into the fault localization, which increases the dimension of the knowledge graph. Simulation results show that the proposed method can achieve 97% accuracy in multiple fault localization scenarios.

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