Yung-Seop Lee

625 total citations
40 papers, 434 citations indexed

About

Yung-Seop Lee is a scholar working on Artificial Intelligence, Information Systems and Electrical and Electronic Engineering. According to data from OpenAlex, Yung-Seop Lee has authored 40 papers receiving a total of 434 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 8 papers in Information Systems and 8 papers in Electrical and Electronic Engineering. Recurrent topics in Yung-Seop Lee's work include Energy Load and Power Forecasting (8 papers), Technology and Data Analysis (5 papers) and Solar Radiation and Photovoltaics (4 papers). Yung-Seop Lee is often cited by papers focused on Energy Load and Power Forecasting (8 papers), Technology and Data Analysis (5 papers) and Solar Radiation and Photovoltaics (4 papers). Yung-Seop Lee collaborates with scholars based in South Korea and United States. Yung-Seop Lee's co-authors include Javier Cabrera, Dhammika Amaratunga, Sunwoo Han, Hyunjoong Kim, Youngdoo Son, Andreas Buja, Seong‐Sik Park, Hyun‐Goo Kim, Changwon Lim and Junghoon Park and has published in prestigious journals such as Bioinformatics, Atmospheric Environment and Expert Systems with Applications.

In The Last Decade

Yung-Seop Lee

33 papers receiving 385 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yung-Seop Lee South Korea	9	123	87	46	45	45	40	434
Yanzhi Chen China	15	75 0.6×	132 1.5×	52 1.1×	98 2.2×	25 0.6×	44	555
Khaled Fawagreh Saudi Arabia	5	112 0.9×	48 0.6×	27 0.6×	25 0.6×	64 1.4×	5	551
Man Liu China	10	91 0.7×	41 0.5×	11 0.2×	60 1.3×	55 1.2×	45	393
Md Abdus Samad South Korea	16	65 0.5×	31 0.4×	144 3.1×	34 0.8×	61 1.4×	72	681
Xuefeng Guan China	15	35 0.3×	83 1.0×	43 0.9×	45 1.0×	114 2.5×	61	876
Deepali India	4	122 1.0×	32 0.4×	40 0.9×	42 0.9×	19 0.4×	11	497
Ye Jiang China	16	210 1.7×	23 0.3×	22 0.5×	27 0.6×	25 0.6×	91	666
Oteng Tabona Botswana	3	179 1.5×	24 0.3×	31 0.7×	37 0.8×	42 0.9×	6	523
Banyatsang Mphago Botswana	4	182 1.5×	23 0.3×	32 0.7×	33 0.7×	42 0.9×	5	524

Countries citing papers authored by Yung-Seop Lee

Since Specialization

Citations

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

Fields of papers citing papers by Yung-Seop Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yung-Seop Lee. 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 Yung-Seop Lee. The network helps show where Yung-Seop Lee may publish in the future.

Co-authorship network of co-authors of Yung-Seop Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Yung-Seop Lee. A scholar is included among the top collaborators of Yung-Seop Lee 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 Yung-Seop Lee. Yung-Seop Lee 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

Lee, Yung-Seop, et al.. (2021). Two-dimensional attention-based multi-input LSTM for time series prediction. Communications for Statistical Applications and Methods. 28(1). 39–57. 1 indexed citations

Lee, Yung-Seop, et al.. (2020). Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks. Applied Sciences. 10(5). 1609–1609. 43 indexed citations

Han, Sunwoo, Hyunjoong Kim, & Yung-Seop Lee. (2020). Double random forest. Machine Learning. 109(8). 1569–1586. 55 indexed citations

Lee, Yung-Seop, et al.. (2019). Initialization by using truncated distributions in artificial neural network. Korean Journal of Applied Statistics. 32(5). 693–702. 1 indexed citations

Kim, Hee-Kyung, et al.. (2017). Cluster analysis by month for meteorological stations using a gridded data of numerical model with temperatures and precipitation. 28(5). 1133–1144. 2 indexed citations

Kang, Namgoo, Min Ho Jung, Hyun-Cheol Jeong, & Yung-Seop Lee. (2015). Comparison of pooled standard deviation and standardized-t bootstrap methods for estimating uncertainty about average methane emission from rice cultivation. Atmospheric Environment. 111. 39–50. 7 indexed citations

Lee, Yung-Seop, et al.. (2015). A meta analysis of the climate change impact on rice yield in South Korea. Journal of the Korean Data and Information Science Society. 26(2). 355–365. 3 indexed citations

Kim, Hee-Kyung & Yung-Seop Lee. (2013). A study on the density analysis of climatological stations using the correlation integral method in the fractal dimension. Journal of the Korean Data and Information Science Society. 24(1). 53–62. 3 indexed citations

Lee, Yung-Seop, et al.. (2013). A study on comparing short-term wind power prediction models in Gunsan wind farm. Journal of the Korean Data and Information Science Society. 24(3). 585–592. 6 indexed citations

10.

Kim, Hee‐Kyung, et al.. (2012). A Study on the Hydrometeorological Observation Positions in the Nak-Dong Upriver by Using Fractal Dimension of the Correlation Intergral. The Korean Data Analysis Society. 14(4). 1919–1928. 1 indexed citations

11.

Lee, Yung-Seop, et al.. (2009). A Study on the Statistical Matching between Survey Data and Administrative Data. 14(1). 82–98. 1 indexed citations

12.

Lee, Yung-Seop, et al.. (2006). A Comparative Study on the Characteristics (Sweat, Stool, Urine, Digestion) of Soyang.Soeumin and Taeyang.Taeumin in Sasang Constitution. Journal of Sasang Constitutional Medicine. 18(1). 107–117. 11 indexed citations

13.

Kim, Hyun‐Goo, et al.. (2006). A Study on Development of a Forecasting Model of Wind Power Generation for Walryong Site. Journal of the Korean Solar Energy Society. 26(2). 27–34. 4 indexed citations

14.

Lee, Yung-Seop, et al.. (2006). A Study on Statistical Forecasting Models of PM10 in Pohang Region by the Variable Transformation. Journal of Korean Society for Atmospheric Environment. 22(5). 614–626. 1 indexed citations

15.

Lee, Yung-Seop, et al.. (2006). Development of the Wind Power Forecasting System, KIER Forecaster. New & Renewable Energy. 2(2). 37–43. 1 indexed citations

16.

Kim, Jung‐Ju, Yung-Seop Lee, & Seong‐Sik Park. (2005). A Clinical Study on the Ordinary Sleeping Patterns of Soyangin. 16(2). 65–72. 5 indexed citations

17.

Kim, Jung‐Ju, Yung-Seop Lee, & Seong‐Sik Park. (2005). A Clinical Study of Sleeping, Stool, Urine According to Taeyangsoyangin(Yangin) and Taeumsoeumin(Eumin). Journal of Sasang Constitutional Medicine. 17(3). 82–90. 7 indexed citations

18.

Lee, Yung-Seop, et al.. (2004). A Study On the Station Density Using Fractal Dimension. The Korean Data Analysis Society. 6(2). 465–471. 1 indexed citations

19.

Choi, Jae Young, et al.. (2004). A Study on the Clinical Features of Ordinary Sleeping Patterns Based on the Sasang Constitution, Using the Logistic Regression. 25(4). 171–179. 8 indexed citations

20.

Lee, Yung-Seop, et al.. (2003). A Comparison of Clustering Algorithm in Data Mining. Journal of the Korean Data and Information Science Society. 14(4). 725–736. 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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