Jian Wang

Orcid: 0000-0001-6187-9803

Affiliations:
  • Kunming University of Science and Technology, Faculty of Electric Power Engineering, Kunming, China
  • Southwest Jiaotong University, School of Electrical Engineering, Chengdu, China (PhD 2022)


According to our database1, Jian Wang authored at least 11 papers between 2022 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

Online presence:

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Bibliography

2024
Crowd Counting Using Meta-Test-Time Adaptation.
Int. J. Neural Syst., November, 2024

Two-Stage Optimal Dispatch of Electricity-Natural Gas Networks Considering Natural Gas Pipeline Leakage and Linepack.
IEEE Trans. Smart Grid, July, 2024

Robust deep Gaussian process-based trustworthy fog-haze-caused pollution flashover prediction approach for overhead contact lines.
Reliab. Eng. Syst. Saf., March, 2024

Uncertainty-aware trustworthy weather-driven failure risk predictor for overhead contact lines.
Reliab. Eng. Syst. Saf., February, 2024

A Real-Time Siamese Network Based on Knowledge Distillation for Insulator Defect Detection of Overhead Contact Lines.
IEEE Trans. Instrum. Meas., 2024

2023
A data-driven integrated framework for predictive probabilistic risk analytics of overhead contact lines based on dynamic Bayesian network.
Reliab. Eng. Syst. Saf., July, 2023

Data-driven lightning-related failure risk prediction of overhead contact lines based on Bayesian network with spatiotemporal fragility model.
Reliab. Eng. Syst. Saf., 2023

Weather-Related Failure Risk Prediction of Overhead Contact Lines Based on Deep Gaussian Process.
Proceedings of the 7th International Conference on High Performance Compilation, 2023

2022
Defect Severity Identification for a Catenary System Based on Deep Semantic Learning.
Sensors, 2022

Predicting wind-caused floater intrusion risk for overhead contact lines based on Bayesian neural network with spatiotemporal correlation analysis.
Reliab. Eng. Syst. Saf., 2022

A survey on the development status and application prospects of knowledge graph in smart grids.
CoRR, 2022


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