In‐Su Han

1.7k total citations
52 papers, 1.3k citations indexed

About

In‐Su Han is a scholar working on Control and Systems Engineering, Electrical and Electronic Engineering and Renewable Energy, Sustainability and the Environment. According to data from OpenAlex, In‐Su Han has authored 52 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Control and Systems Engineering, 15 papers in Electrical and Electronic Engineering and 10 papers in Renewable Energy, Sustainability and the Environment. Recurrent topics in In‐Su Han's work include Fuel Cells and Related Materials (13 papers), Fault Detection and Control Systems (12 papers) and Electrocatalysts for Energy Conversion (10 papers). In‐Su Han is often cited by papers focused on Fuel Cells and Related Materials (13 papers), Fault Detection and Control Systems (12 papers) and Electrocatalysts for Energy Conversion (10 papers). In‐Su Han collaborates with scholars based in South Korea, United States and United Kingdom. In‐Su Han's co-authors include Chang‐Bock Chung, Chonghun Han, Sang-Kyun Park, Paul Dawson, Jee‐Hoon Jeong, James B. Riggs, Young Hak Lee, Jinwoo Shin, Jae Wook Lee and Catherine Black and has published in prestigious journals such as Journal of Power Sources, Chemical Engineering Journal and International Journal of Hydrogen Energy.

In The Last Decade

In‐Su Han

47 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
In‐Su Han South Korea 20 464 314 267 239 176 52 1.3k
Chang‐Bock Chung South Korea 14 242 0.5× 495 1.6× 158 0.6× 195 0.8× 92 0.5× 33 1.0k
Guangbin Liu China 26 604 1.3× 377 1.2× 149 0.6× 742 3.1× 73 0.4× 194 2.4k
Jin Zhao China 18 542 1.2× 148 0.5× 43 0.2× 422 1.8× 488 2.8× 71 1.6k
Chenxi Wang China 25 185 0.4× 458 1.5× 955 3.6× 525 2.2× 57 0.3× 79 2.8k
Hongmei Zhang China 24 142 0.3× 88 0.3× 64 0.2× 284 1.2× 89 0.5× 156 1.8k
Mohammad Shakeri Malaysia 16 648 1.4× 235 0.7× 248 0.9× 95 0.4× 72 0.4× 32 1.1k
Rames C. Panda India 22 193 0.4× 839 2.7× 79 0.3× 149 0.6× 38 0.2× 119 1.8k
Zhao Yang China 32 218 0.5× 90 0.3× 142 0.5× 1.4k 6.0× 91 0.5× 203 3.0k
Guoxiang Li China 24 348 0.8× 43 0.1× 189 0.7× 456 1.9× 301 1.7× 162 1.9k
Lixin Lu China 24 132 0.3× 96 0.3× 168 0.6× 557 2.3× 68 0.4× 190 2.0k

Countries citing papers authored by In‐Su Han

Since Specialization
Citations

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

Fields of papers citing papers by In‐Su Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by In‐Su Han. 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 In‐Su Han. The network helps show where In‐Su Han may publish in the future.

Co-authorship network of co-authors of In‐Su Han

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

All Works

20 of 20 papers shown
1.
Hong, Seokyoung, Hyungtae Cho, Jongkoo Lim, et al.. (2022). Time-series clustering approach for training data selection of a data-driven predictive model: Application to an industrial bio 2,3-butanediol distillation process. Computers & Chemical Engineering. 161. 107758–107758. 12 indexed citations
2.
Han, In‐Su & Jennifer Gillenwater. (2020). MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search.. International Conference on Artificial Intelligence and Statistics. 2797–2807. 2 indexed citations
3.
Han, In‐Su & Chang‐Bock Chung. (2017). A hybrid model combining a support vector machine with an empirical equation for predicting polarization curves of PEM fuel cells. International Journal of Hydrogen Energy. 42(10). 7023–7028. 29 indexed citations
4.
Han, In‐Su, Sang-Kyun Park, & Chang‐Bock Chung. (2016). Modeling and operation optimization of a proton exchange membrane fuel cell system for maximum efficiency. Energy Conversion and Management. 113. 52–65. 85 indexed citations
5.
Han, In‐Su, et al.. (2016). Peach skin powder inhibits oxidation in cooked turkey meat. Poultry Science. 95(10). 2435–2440. 7 indexed citations
6.
Han, In‐Su, et al.. (2015). Modeling of a PEM Fuel Cell Stack using Partial Least Squares and Artificial Neural Networks. Korean Chemical Engineering Research. 53(2). 236–242. 12 indexed citations
7.
Northcutt, J.K., et al.. (2013). Antioxidant activity of carnosine extracted from various poultry tissues. Poultry Science. 92(2). 444–453. 24 indexed citations
8.
Han, In‐Su, et al.. (2013). PEM fuel-cell stack design for improved fuel utilization. International Journal of Hydrogen Energy. 38(27). 11996–12006. 63 indexed citations
9.
Northcutt, J.K., et al.. (2011). Effect of stress on carnosine levels in brain, breast, and thigh of broilers. Poultry Science. 90(10). 2348–2354. 12 indexed citations
10.
Kang, Hyunmin, In‐Su Han, & Chan Lim. (2007). Development of Composite Bipolar Plate for PEMFC. New & Renewable Energy. 3(4). 3–7.
11.
Cooksey, Kay, et al.. (2006). Quality of Fresh Chicken Breasts Using a Combination of Modified Atmosphere Packaging and Chlorine Dioxide Sachets. Journal of Food Protection. 69(8). 1991–1996. 27 indexed citations
12.
Dawson, Paul, et al.. (2006). Residence time and food contact time effects on transfer of Salmonella Typhimurium from tile, wood and carpet: testing the five-second rule. Journal of Applied Microbiology. 0(0). 2670577961–???. 62 indexed citations
13.
Han, In‐Su. (2006). A Novel Tunable Transconductance Amplifier Based on Voltage-Controlled Resistance by MOS Transistors. IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing. 53(8). 662–666. 22 indexed citations
14.
Han, In‐Su, Chonghun Han, & Chang‐Bock Chung. (2004). Optimization of the Air- and Gas-supply Network of a Chemical Plant. Process Safety and Environmental Protection. 82(10). 1337–1343. 16 indexed citations
15.
Han, In‐Su, Minjin Kim, Chang-Hyun Lee, et al.. (2003). Application of partial least squares methods to a terephthalic acid manufacturing process for product quality control. Korean Journal of Chemical Engineering. 20(6). 977–984. 12 indexed citations
16.
Han, In‐Su, et al.. (2002). Improved evolutionary operation based on D-optimal design and response surface method. Korean Journal of Chemical Engineering. 19(4). 535–544. 3 indexed citations
17.
Han, In‐Su, et al.. (2002). Iterative Error-based Nonlinear PLS Method for Nonlinear Chemical Process Modeling.. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN. 35(7). 613–625. 5 indexed citations
18.
Han, In‐Su, Chang‐Bock Chung, & James B. Riggs. (2000). Modeling of a fluidized catalytic cracking process. Computers & Chemical Engineering. 24(2-7). 1681–1687. 25 indexed citations
19.
Han, In‐Su, Chang‐Bock Chung, & Jae Wook Lee. (2000). Optimal Curing of Rubber Compounds with Reversion Type Cure Behavior. Rubber Chemistry and Technology. 73(1). 101–113. 36 indexed citations
20.
Han, In‐Su, et al.. (1999). Optimal cure steps for product quality in a tire curing process. Journal of Applied Polymer Science. 74(8). 2063–2063. 2 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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