Kin Gwn Lore

2.3k total citations · 1 hit paper
19 papers, 1.5k citations indexed

About

Kin Gwn Lore is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition and Aerospace Engineering. According to data from OpenAlex, Kin Gwn Lore has authored 19 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Mechanics, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Aerospace Engineering. Recurrent topics in Kin Gwn Lore's work include Aerodynamics and Acoustics in Jet Flows (4 papers), Combustion and flame dynamics (4 papers) and Wind and Air Flow Studies (3 papers). Kin Gwn Lore is often cited by papers focused on Aerodynamics and Acoustics in Jet Flows (4 papers), Combustion and flame dynamics (4 papers) and Wind and Air Flow Studies (3 papers). Kin Gwn Lore collaborates with scholars based in United States, India and China. Kin Gwn Lore's co-authors include Soumik Sarkar, Adedotun Akintayo, Daniel Stoecklein, Baskar Ganapathysubramanian, Michael Giering, Edgar A. Bernal, Soumalya Sarkar, Kishore Reddy, Devu Manikantan Shila and Zhanhong Jiang and has published in prestigious journals such as Scientific Reports, Pattern Recognition and AIAA Journal.

In The Last Decade

Kin Gwn Lore

18 papers receiving 1.4k 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.

LLNet: A deep autoencoder approach to natural low-light image enhancement

2016 1.2k citations Kin Gwn Lore, Adedotun Akintayo et al. Pattern Recognition profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kin Gwn Lore United States	12	1.2k	523	118	88	81	19	1.5k
Hongwei Yong Hong Kong	15	1.5k 1.3×	905 1.7×	170 1.4×	134 1.5×	81 1.0×	24	1.7k
Wenxiu Sun Hong Kong	22	1.5k 1.3×	356 0.7×	78 0.7×	172 2.0×	131 1.6×	60	1.7k
Rui Song China	24	992 0.9×	906 1.7×	117 1.0×	281 3.2×	155 1.9×	95	1.7k
Sumohana S. Channappayya India	17	1.3k 1.2×	616 1.2×	130 1.1×	71 0.8×	46 0.6×	91	1.7k
Xiangyu Chen China	14	930 0.8×	448 0.9×	63 0.5×	132 1.5×	80 1.0×	53	1.2k
Wei‐Sheng Lai United States	19	2.1k 1.9×	967 1.8×	88 0.7×	62 0.7×	62 0.8×	27	2.4k
Xiaoyan Sun China	25	2.5k 2.1×	756 1.4×	165 1.4×	275 3.1×	73 0.9×	132	2.8k
Daniel Gläsner United States	10	1.6k 1.4×	848 1.6×	70 0.6×	168 1.9×	96 1.2×	17	1.8k
Ning He China	13	884 0.8×	279 0.5×	305 2.6×	96 1.1×	95 1.2×	87	1.2k

Countries citing papers authored by Kin Gwn Lore

Since Specialization

Citations

This map shows the geographic impact of Kin Gwn Lore'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 Kin Gwn Lore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kin Gwn Lore more than expected).

Fields of papers citing papers by Kin Gwn Lore

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kin Gwn Lore. 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 Kin Gwn Lore. The network helps show where Kin Gwn Lore may publish in the future.

Co-authorship network of co-authors of Kin Gwn Lore

This figure shows the co-authorship network connecting the top 25 collaborators of Kin Gwn Lore. A scholar is included among the top collaborators of Kin Gwn Lore 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 Kin Gwn Lore. Kin Gwn Lore is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

Zhang, Yifan, et al.. (2024). Machine Learning Aided Low-Order Predictions of Fan Stage Broadband Interaction Noise. AIAA Journal. 62(6). 2174–2185.

Winkler, Julian, et al.. (2023). Machine Learning Aided Fan Broadband Interaction Noise Prediction for Leaned and Swept Fans. 2 indexed citations

Shen, Hao, et al.. (2022). Fan Wake Prediction Via Machine Learning. 28th AIAA/CEAS Aeroacoustics 2022 Conference. 1 indexed citations

Akintayo, Adedotun, Kin Gwn Lore, Soumalya Sarkar, & Soumik Sarkar. (2020). Prognostics of Combustion Instabilities from Hi-speed Flame Video using A Deep Convolutional Selective Autoencoder. International Journal of Prognostics and Health Management. 7(4). 13 indexed citations

Liu, Chao, Kin Gwn Lore, Zhanhong Jiang, & Soumik Sarkar. (2020). Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems. Knowledge-Based Systems. 211. 106527–106527. 18 indexed citations

Lore, Kin Gwn, K. Krishna Reddy, Michael Giering, & Edgar A. Bernal. (2019). Generative Adversarial Networks for Spectral Super-Resolution and Bidirectional RGB-To-Multispectral Mapping. 926–933. 19 indexed citations

Lore, Kin Gwn, Devu Manikantan Shila, & Lingyu Ren. (2018). Detecting Data Integrity Attacks on Correlated Solar Farms Using Multi-layer Data Driven Algorithm. 1–9. 15 indexed citations

Lore, Kin Gwn, Kishore Reddy, Michael Giering, & Edgar A. Bernal. (2018). Generative Adversarial Networks for Depth Map Estimation from RGB Video. 1258–12588. 36 indexed citations

Chamie, Mahmoud El, Kin Gwn Lore, Devu Manikantan Shila, & Amit Surana. (2018). Physics-Based Features for Anomaly Detection in Power Grids with Micro-PMUs. 1–7. 15 indexed citations

10.

Stoecklein, Daniel, et al.. (2017). Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data. Scientific Reports. 7(1). 46368–46368. 61 indexed citations

11.

Lore, Kin Gwn, et al.. (2017). A deep learning framework for causal shape transformation. Neural Networks. 98. 305–317. 18 indexed citations

12.

Liu, Chao, Kin Gwn Lore, & Soumik Sarkar. (2017). Data-driven root-cause analysis for distributed system anomalies. 6 indexed citations

13.

Balu, Aditya, et al.. (2016). A Deep 3D Convolutional Neural Network Based Design for Manufacturability Framework. arXiv (Cornell University). 9 indexed citations

14.

Lore, Kin Gwn, et al.. (2016). Deep value of information estimators for collaborative human-machine information gathering. arXiv (Cornell University). 3. 6 indexed citations

15.

Lore, Kin Gwn, Adedotun Akintayo, & Soumik Sarkar. (2016). LLNet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognition. 61. 650–662. 1222 indexed citations breakdown →

16.

Sarkar, Soumalya, Devesh K. Jha, Kin Gwn Lore, Soumik Sarkar, & Asok Ray. (2016). Multimodal spatiotemporal information fusion using neural-symbolic modeling for early detection of combustion instabilities. 33. 4918–4923. 3 indexed citations

17.

Lore, Kin Gwn, et al.. (2015). Hierarchical Feature Extraction for Efficient Design of Microfluidic Flow Patterns. Iowa State University Digital Repository (Iowa State University). 44. 213–225. 12 indexed citations

18.

Sarkar, Soumalya, Kin Gwn Lore, & Soumik Sarkar. (2015). Early detection of combustion instability by neural-symbolic analysis on hi-speed video. neural information processing systems. 93–101. 16 indexed citations

19.

Sarkar, Soumalya, Kin Gwn Lore, Soumik Sarkar, et al.. (2015). Early Detection of Combustion Instability from Hi-speed Flame Images via Deep Learning and Symbolic Time Series Analysis. Annual Conference of the PHM Society. 7(1). 22 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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