Kang‐Di Lu

2.9k total citations · 2 hit papers
49 papers, 2.3k citations indexed

About

Kang‐Di Lu is a scholar working on Control and Systems Engineering, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Kang‐Di Lu has authored 49 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Control and Systems Engineering, 18 papers in Artificial Intelligence and 15 papers in Electrical and Electronic Engineering. Recurrent topics in Kang‐Di Lu's work include Smart Grid Security and Resilience (14 papers), Network Security and Intrusion Detection (14 papers) and Metaheuristic Optimization Algorithms Research (8 papers). Kang‐Di Lu is often cited by papers focused on Smart Grid Security and Resilience (14 papers), Network Security and Intrusion Detection (14 papers) and Metaheuristic Optimization Algorithms Research (8 papers). Kang‐Di Lu collaborates with scholars based in China, Qatar and United States. Kang‐Di Lu's co-authors include Guo‐Qiang Zeng, Min-Rong Chen, Zheng‐Guang Wu, Wuneng Zhou, Jian Weng, Jie Chen, Wei Du, Tingwen Huang, Yuxing Dai and Xizhao Luo and has published in prestigious journals such as Angewandte Chemie International Edition, SHILAP Revista de lepidopterología and IEEE Transactions on Power Systems.

In The Last Decade

Kang‐Di Lu

47 papers receiving 2.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization

2018 327 citations Guo‐Qiang Zeng, Wuneng Zhou et al. profile →
Evolutionary Adversarial Autoencoder for Unsupervised Anomaly Detection of Industrial Internet of Things

2025 21 citations Guo‐Qiang Zeng, Kang‐Di Lu et al. IEEE Transactions on Reliability profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Kang‐Di Lu China	24	971	813	683	332	174	49	2.3k
Fang Fang China	37	1.2k 1.2×	1.3k 1.6×	566 0.8×	487 1.5×	23 0.1×	220	3.7k
Bo Chen China	35	2.8k 2.9×	2.2k 2.7×	320 0.5×	651 2.0×	95 0.5×	240	4.5k
Xia Chen China	36	2.0k 2.1×	1.6k 2.0×	231 0.3×	774 2.3×	30 0.2×	186	3.8k
Zhe Wu United States	33	373 0.4×	1.8k 2.2×	433 0.6×	142 0.4×	29 0.2×	166	3.3k
Jianhui Wang United States	35	2.8k 2.9×	1.9k 2.4×	310 0.5×	288 0.9×	58 0.3×	104	3.7k
Narinder Singh India	18	437 0.5×	284 0.3×	677 1.0×	205 0.6×	22 0.1×	100	1.7k
Lihong Guo China	27	376 0.4×	294 0.4×	1.2k 1.7×	206 0.6×	48 0.3×	83	2.7k
Huaizhi Wang China	33	3.6k 3.7×	1.1k 1.3×	1.3k 1.9×	334 1.0×	19 0.1×	136	4.9k
Weimin Zhong China	24	258 0.3×	1.1k 1.3×	387 0.6×	420 1.3×	23 0.1×	178	2.3k
Jing Wang China	28	1.1k 1.2×	1.4k 1.7×	490 0.7×	824 2.5×	8 0.0×	316	3.5k

Countries citing papers authored by Kang‐Di Lu

Since Specialization

Citations

This map shows the geographic impact of Kang‐Di Lu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kang‐Di Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kang‐Di Lu more than expected).

Fields of papers citing papers by Kang‐Di Lu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kang‐Di Lu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kang‐Di Lu. The network helps show where Kang‐Di Lu may publish in the future.

Co-authorship network of co-authors of Kang‐Di Lu

This figure shows the co-authorship network connecting the top 25 collaborators of Kang‐Di Lu. A scholar is included among the top collaborators of Kang‐Di Lu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kang‐Di Lu. Kang‐Di Lu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Lu, Kang‐Di, et al.. (2025). Multi-Objective Discrete Extremal Optimization of Variable-Length Blocks-Based CNN by Joint NAS and HPO for Intrusion Detection in IIoT. IEEE Transactions on Dependable and Secure Computing. 22(4). 4266–4283. 16 indexed citations

Lu, Kang‐Di, et al.. (2025). MoARNN-AM: Multi-Objective Automated Recurrent Neural Network With Attention Mechanism for Cyber-Attack Detection of UAV. IEEE Transactions on Consumer Electronics. 72(1). 1738–1749.

Zeng, Guo‐Qiang, et al.. (2025). Evolutionary Adversarial Autoencoder for Unsupervised Anomaly Detection of Industrial Internet of Things. IEEE Transactions on Reliability. 74(3). 3454–3468. 21 indexed citations breakdown →

Zeng, Guo‐Qiang, et al.. (2025). MoCC-BD-FID: Multi-Objective Clustering Combination-Based Backdoor Defense for Federated Intrusion Detection of Industrial Control Systems. IEEE Transactions on Information Forensics and Security. 20. 6868–6883. 1 indexed citations

Zeng, Guo‐Qiang, Zhen Qin, Kang‐Di Lu, & Limin Li. (2024). CMOPEO-OP: Constrained multi-objective population extremal optimization-based optimal planning of standalone microgrids. Swarm and Evolutionary Computation. 92. 101787–101787. 5 indexed citations

Zeng, Guo‐Qiang, et al.. (2024). Automated federated learning for intrusion detection of industrial control systems based on evolutionary neural architecture search. Computers & Security. 143. 103910–103910. 13 indexed citations

Zeng, Guo‐Qiang, et al.. (2024). Automated federated learning‐based adversarial attack and defence in industrial control systems. SHILAP Revista de lepidopterología. 6(2). 13 indexed citations

Wang, Rong, Guo‐Qiang Zeng, & Kang‐Di Lu. (2024). MO-Transformer-IDS: Multi-objective Optimization-based Transformer for Multi-Classification Intrusion Detection of Cyber-Physical Power Systems. 13–18.

Zhang, Yu, Guo‐Qiang Zeng, Min-Rong Chen, et al.. (2024). DoFA: Adversarial examples detection for SAR images by dual-objective feature attribution. Expert Systems with Applications. 255. 124705–124705. 4 indexed citations

10.

Zeng, Guo‐Qiang, et al.. (2024). MoAR-CNN: Multi-Objective Adversarially Robust Convolutional Neural Network for SAR Image Classification. IEEE Transactions on Emerging Topics in Computational Intelligence. 9(1). 57–74. 5 indexed citations

11.

Lu, Kang‐Di, Le Zhou, & Zheng‐Guang Wu. (2023). Representation-Learning-Based CNN for Intelligent Attack Localization and Recovery of Cyber-Physical Power Systems. IEEE Transactions on Neural Networks and Learning Systems. 35(5). 6145–6155. 47 indexed citations

12.

Lu, Kang‐Di, Zheng‐Guang Wu, & Tingwen Huang. (2022). Differential Evolution-Based Three Stage Dynamic Cyber-Attack of Cyber-Physical Power Systems. IEEE/ASME Transactions on Mechatronics. 28(2). 1137–1148. 110 indexed citations

13.

Lu, Kang‐Di & Zheng‐Guang Wu. (2022). An Ensemble Learning-Based Cyber-Attacks Detection Method of Cyber-Physical Power Systems. 1029–1034. 5 indexed citations

14.

Lu, Kang‐Di & Zheng‐Guang Wu. (2022). Multi-Objective False Data Injection Attacks of Cyber–Physical Power Systems. IEEE Transactions on Circuits & Systems II Express Briefs. 69(9). 3924–3928. 52 indexed citations

15.

Lu, Kang‐Di, Guo‐Qiang Zeng, Xizhao Luo, et al.. (2021). Evolutionary Deep Belief Network for Cyber-Attack Detection in Industrial Automation and Control System. IEEE Transactions on Industrial Informatics. 17(11). 7618–7627. 77 indexed citations

16.

Chen, Min-Rong, et al.. (2021). An improved bat algorithm hybridized with extremal optimization and Boltzmann selection. Expert Systems with Applications. 175. 114812–114812. 33 indexed citations

17.

Chen, Min-Rong, et al.. (2019). An Electric Load Forecasting Model Based on BP Neural Network and Improved Bat Algorithm Hybridized with Extremal Optimization. 4673–4678. 1 indexed citations

18.

Lu, Kang‐Di, et al.. (2018). Design of PID controller based on a self-adaptive state-space predictive functional control using extremal optimization method. Journal of the Franklin Institute. 355(5). 2197–2220. 48 indexed citations

19.

Zeng, Guo‐Qiang, Kang‐Di Lu, Jie Chen, et al.. (2014). An Improved Real‐Coded Population‐Based Extremal Optimization Method for Continuous Unconstrained Optimization Problems. Mathematical Problems in Engineering. 2014(1). 6 indexed citations

20.

Tsai, Hui‐Lien, et al.. (2005). Crystal Engineering: Toward Intersecting Channels from a Neutral Network with a bcu‐Type Topology. Angewandte Chemie International Edition. 44(37). 6063–6067. 184 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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