Jong-Myon Kim

9.7k total citations · 3 hit papers
291 papers, 6.9k citations indexed

About

Jong-Myon Kim is a scholar working on Control and Systems Engineering, Mechanical Engineering and Civil and Structural Engineering. According to data from OpenAlex, Jong-Myon Kim has authored 291 papers receiving a total of 6.9k indexed citations (citations by other indexed papers that have themselves been cited), including 144 papers in Control and Systems Engineering, 134 papers in Mechanical Engineering and 48 papers in Civil and Structural Engineering. Recurrent topics in Jong-Myon Kim's work include Machine Fault Diagnosis Techniques (129 papers), Gear and Bearing Dynamics Analysis (81 papers) and Fault Detection and Control Systems (59 papers). Jong-Myon Kim is often cited by papers focused on Machine Fault Diagnosis Techniques (129 papers), Gear and Bearing Dynamics Analysis (81 papers) and Fault Detection and Control Systems (59 papers). Jong-Myon Kim collaborates with scholars based in South Korea, United States and Bangladesh. Jong-Myon Kim's co-authors include M. M. Manjurul Islam, Cheol Hong Kim, Sheraz Ali Khan, Jae‐Young Kim, Myeongsu Kang, Rafia Nishat Toma, Muhammad Sohaib, Zahoor Ahmad, Alexander E. Prosvirin and Farzin Piltan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Industrial Electronics.

In The Last Decade

Jong-Myon Kim

258 papers receiving 6.7k 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.

Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach

2019 259 citations M. M. Manjurul Islam, Jong-Myon Kim et al. profile →
Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

2020 204 citations Jong-Myon Kim et al. Sensors profile →
A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction

2025 18 citations Muhammad Siddique, Muhammad Umar et al. Sensors profile →

Peers

Countries citing papers authored by Jong-Myon Kim

Since Specialization

Citations

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

Fields of papers citing papers by Jong-Myon Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jong-Myon Kim. 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 Jong-Myon Kim. The network helps show where Jong-Myon Kim may publish in the future.

Co-authorship network of co-authors of Jong-Myon Kim

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

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction breakdown → 2025 ·Sensors ·Muhammad Siddique, (unknown), Muhammad Umar, Cheol Hong Kim, Jong-Myon Kim	18
2	A remaining useful lifetime prediction model for concrete structures using Mann-Whitney U test state indicator and deep learning 2024 ·Mechanical Systems and Signal Processing ·(unknown), Zahoor Ahmad, (unknown), Jong-Myon Kim	11
3	Advanced Bearing-Fault Diagnosis and Classification Using Mel-Scalograms and FOX-Optimized ANN 2024 ·Sensors ·Muhammad Siddique, (unknown), (unknown), Muhammad Umar, (unknown), Dongkoo Shon, Tae Hyun Yoon, Dae-Seung Yoo, Jong-Myon Kim	19
4	Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network 2024 ·Sensors ·(unknown), Zahoor Ahmad, Jong-Myon Kim	6
5	Gear fault diagnosis based on bidimensional time-frequency information theoretic features and error-correcting output codes: A multi-class support vector machine 2024 ·Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ·Akhand Rai, Jong-Myon Kim, Anil Kumar, P. S. Balaji	0
6	Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach 2024 ·Applied Sciences ·(unknown), N. Ullah, Muhammad Siddique, Zahoor Ahmad, Jong-Myon Kim	9
7	Centrifugal Pump Fault Diagnosis Based on a Novel SobelEdge Scalogram and CNN 2023 ·Sensors ·(unknown), Zahoor Ahmad, Muhammad Siddique, Niamat Ullah, Jong-Myon Kim	25
8	Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning 2021 ·Sensors ·Md Junayed Hasan, M. M. Manjurul Islam, Jong-Myon Kim	26
9	Deep Learning-Based Adaptive Neural-Fuzzy Structure Scheme for Bearing Fault Pattern Recognition and Crack Size Identification 2021 ·Sensors ·Farzin Piltan, (unknown), Jong-Myon Kim	4
10	A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning 2021 ·IEEE Access ·Md Junayed Hasan, Akhand Rai, Zahoor Ahmad, Jong-Myon Kim	38
11	Health State Classification of a Spherical Tank Using a Hybrid Bag of Features and K-Nearest Neighbor 2020 ·Applied Sciences ·Md Junayed Hasan, Jae‐Young Kim, Cheol Hong Kim, Jong-Myon Kim	13
12	Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals 2020 ·Applied Sciences ·Md Junayed Hasan, Dongkoo Shon, Kichang Im, Hyun‐Kyun Choi, Dae-Seung Yoo, Jong-Myon Kim	37
13	A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions 2020 ·Sensors ·Md Junayed Hasan, Muhammad Sohaib, Jong-Myon Kim	28
14	Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features 2019 ·Entropy ·(unknown), (unknown), Jae‐Young Kim, Muhammad Sohaib, Jong-Myon Kim	7
15	Deep Learning Object-Impulse Detection for Enhancing Leakage Detection of a Boiler Tube Using Acoustic Emission Signal 2019 ·Applied Sciences ·(unknown), Jae‐Young Kim, Cheol Hong Kim, Jong-Myon Kim	9
16	A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals 2019 ·Brain Sciences ·Md Junayed Hasan, Jong-Myon Kim	52
17	Punched H2Ti12O25 anode and activated carbon cathode for high energy/high power hybrid supercapacitors 2018 ·Energy ·Seung-Hwan Lee, Jong-Myon Kim	11
18	Bearing Fault Diagnosis under Variable Rotational Speeds Using Stockwell Transform-Based Vibration Imaging and Transfer Learning 2018 ·Applied Sciences ·Md Junayed Hasan, Jong-Myon Kim	92
19	A Reliable Health Indicator for Fault Prognosis of Bearings 2018 ·Sensors ·(unknown), Sheraz Ali Khan, Dongkoo Shon, Kichang Im, Jeong-Ho Park, (unknown), Byungtae Jang, Jong-Myon Kim	49
20	Primitive audio genre classification:An investigation of feature vector optimization 2012 ·VBN Forskningsportal (Aalborg Universitet) ·Mohammad A. Haque, (unknown), Jong-Myon Kim	1

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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