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.
Sensorless drive of surface-mounted permanent-magnet motor by high-frequency signal injection based on magnetic saliency
2003424 citationsSeung‐Ki Sul, Jung-Ik Ha et al.IEEE Transactions on Industry Applicationsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Jung-Ik Ha'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 Jung-Ik Ha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jung-Ik Ha more than expected).
This network shows the impact of papers produced by Jung-Ik Ha. 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 Jung-Ik Ha. The network helps show where Jung-Ik Ha may publish in the future.
Co-authorship network of co-authors of Jung-Ik Ha
This figure shows the co-authorship network connecting the top 25 collaborators of Jung-Ik Ha.
A scholar is included among the top collaborators of Jung-Ik Ha 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 Jung-Ik Ha. Jung-Ik Ha is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Song, Taeksun, Hyeyoung Lee, Jung-Ik Ha, et al.. (2002). Reclassification of a Carboxydobacterium, Acinetobacter sp. Strain JC1 DSM3803, as Mycobacterium sp. Strain JC1 DSM 3803. The Journal of Microbiology. 40(3). 237–240.13 indexed citations
Kim, Jong-Keun, et al.. (2000). Probiotic effects of Lactobacillus reuteri BSA-131 on piglets. KRIBB Repository.3 indexed citations
13.
Lee, Hun Joo, Chan Sun Park, Seung Ho Kim, et al.. (1999). Identification and characterization of bacteriocin-producing lactic acid bacteria isolated from Kimchi. Journal of Microbiology and Biotechnology. 9(3). 282–291.13 indexed citations
14.
Park, Ju Young, et al.. (1998). Distribution of ubiquinone systems in fungi. KRIBB Repository. 8(1). 27–31.5 indexed citations
15.
Lee, Byung Uk, et al.. (1997). Development of a program for fragment assembly from DNA sequence data. KRIBB Repository.
16.
Lee, Hun Joo, et al.. (1997). Identification of Leuconostoc Strains Isolated from Kimchi Using Carbon-source Utilization Patterns. The Journal of Microbiology. 35(1). 10–14.8 indexed citations
17.
Lee, Jung‐Sook, et al.. (1996). Identification of lactic acid bacteria from Kimchi by cellular FAMEs analysis. KRIBB Repository.9 indexed citations
18.
Shin, Yong Kook, et al.. (1996). Isoprenoid quinone profiles of the Leclercia adecarboxylata KCTC 1036T. Journal of Microbiology and Biotechnology. 6(1). 68–69.117 indexed citations
19.
Lee, Hun Joo, et al.. (1996). Analysis of Cellular Fatty Acid Methyl Esters (FAMEs) for the Identification of Leuconostoc Strains Isolated from Kimchi. The Journal of Microbiology. 34(3). 225–228.6 indexed citations
20.
Shin, Yong Kook, et al.. (1996). Microbial DNA base composition(G+C mol%) and its taxonomic implications. KRIBB Repository. 6(1). 72–77.3 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.