Jing Lu

Prof. Jing Lu


Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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11 September 2020 in Applied Stochastic Models in Business and Industry
Verified
Journal Article
Closed access
PDF file
JLJing Lu
Jing Lu
SWShihong Wu
Shihong Wu
HCHanlei Cheng
Hanlei Cheng
BSBin Song
Bin Song
ZXZhiyu Xiang
Zhiyu Xiang

2 August 2020 in Computational Intelligence
Verified
Journal Article
Closed access
PDF file
JLJing Lu
Jing Lu
SWShihong Wu
Shihong Wu
HCHanlei Cheng
Hanlei Cheng
ZXZhiyu Xiang
Zhiyu Xiang


13 August 2013 in Optik
Verified
Journal Article
Closed access
JLJing Lu
Jing Lu
MXMin Xia
Min Xia
WLWei Li
Wei Li
WGWen-Ping Guo
Wen-Ping Guo
KYKe-Cheng Yang
Ke-Cheng Yang

6 December 2025 in Internet of Things
Verified
Journal Article
Open access
HCHanlei Cheng
Hanlei Cheng
SLSio-long Lo
Sio-long Lo
JLJing Lu
Jing Lu

17 January 2024 in IET Blockchain
Verified
Journal Article
Open access
JLJing Lu
Jing Lu
WYWeidong Yu
Weidong Yu
SWShihong Wu
Shihong Wu
In order to improve the precise control level of power grid infrastructure investment, this paper proposes a monitoring and early warning method of power grid infrastructure investment progress based on building information model and blockchain. By capturing the project construction progress images, the features of the power grid infrastructure are extracted automatically. Combined with the technical characteristics of distributed, tamper‐proof, and traceable blockchain, statistical indicators are generated automatically, monitoring and early warning of the investment progress execution deviation are triggered by the rules running on the smart contracts. The case study results show that the mean absolute error of the target image recognition method based on the image features is 4.32%, and the prediction accuracy of the incoming line is better than that of the engineering civil and substation. The early warning model of investment statistics based on smart contracts can automatically monitor the investment progress and generate early warnings, which provides a basis for the dynamic adjustment of the investment plan, and effectively improves the refined management level of power grid infrastructure investment projects.