Sungkyunkwan University · 工学
Professor Jong-Seok Lee's research lab specializes in environmental hydraulics, machine learning for data-intensive science, and nanomaterial synthesis. The lab investigates hydraulic resistance in vegetated rivers, develops robust machine learning models for imbalanced data and adversarial attacks in recommender systems, and explores the controlled synthesis of gold nanorods using biochemical and physical parameters. A recurring theme is the integration of empirical field data with advanced statistical and computational modeling to address real-world challenges in hydrology, environmental engineering, and materials science.
Figures are computed from collected data and may differ slightly.
This study extends the earlier contribution of Julien and Wargadalam in 1995. A larger database for the downstream hydraulic geometry of alluvial channels is examined through a nonlinear regression analysis. The database consists of a total of 1,485 measurements, 1,125 of which describe field data used for model calibration. The remaining 360 field and laboratory measurements are used for validation. The data used for validation include sand-bed, gravel-bed, and cobble-bed streams with meanderin
Recommender systems rely on the opinions of many users to predict the preferences of potential customers. These systems have been broadly used to make quality recommendations to increase sales. However, recommender systems are vulnerable to even small data inputs of malicious information. Inappropriate products can be offered to users by injecting a few unscrupulous “shilling” profiles into the recommender system. This research proposes to identify a cluster of profiles by focusing on “filler” r
This paper presents a modification of Quinlan’s C4.5 algorithm for imbalanced data classification. While the C4.5 algorithm uses the difference in information entropy to determine the goodness of a split, the proposed method, which is named AUC4.5, examines the difference in the area under the ROC curve (AUC) of a split. It implies that our method attempts to maximize the AUC value of a trained decision tree in order to cope with class imbalance in data. An extensive experimental study was perfo
Here, we systematically investigated the independent, multiple, and synergic effects of three major components, namely, ascorbic acid (AA), seed, and silver ions (Ag<sup>+</sup>), on the characteristics of gold nanorods (GNRs), i.e., longitudinal localized surface plasmon resonance (LSPR) peak position, shape, size, and monodispersity. To quantitatively assess the shape and dimensions of GNRs, we used an automated transmission electron microscopy image analysis method using a MATLAB-based code d
In binary classifications, a decision tree learned from unbalanced data typically creates an important challenge related to the high misclassification rate of the minority class. Assigning different misclassification costs can address this problem, though usually at the cost of accuracy for the majority class. This effect can be particularly hazardous if the costs cannot be specified precisely. When the costs are unknown or difficult to determine, decision makers may prefer a classifier with mor
본 연구는 흐름 저항에 대한 분석을 위해 초본 281개, 관목 150개, 교목 308개의 현장실측 자료로 구성된 739개 식생하천을 대상으로 수행되었다. 실측자료의 Manning 조도계수 분포는 초본자료에서 0.015~0.250, 관목자료에서 0.016~0.250, 교목자료에서 0.018~0.310의 범위를 갖는다. 이들 조도계수의 중요한 분포경향은 Darcy-Weisbach (또는 Manning의 조도계수)와 유량, 마찰경사 및 상대 잠수비에 대한 관계식으로 제시하였다. 식생하천에서 Darcy-Weisbach와 Manning 조도계수에 관한 회귀 방정식은 <TEX>$f_{veg}=0.436Q^{-0.363}$</TEX>, <TEX>$f_{veg}=3.305S_f^{0.508}$</TEX>와 <TEX>$n_{veg}=0.061Q^{-0.124}$</TEX>, <TEX>$n_{veg}=0.144S_f^{0.199}$</TEX> 및 <TEX>$V=5.3(h/d_{50})^{1/8.3}{
본 연구는 충적하천에서 실측된 현장자료를 이용하여 흐름에 관한 저항계수와 관계식을 분석하고 유도하였으며, 연구에는 자연하천 자료 1,865개와 식생하천 자료 739개가 포함된 2,604개 자료가 사용되었다. 회귀분석에 의해 Manning 조도계수와 Darcy-Weisbach 마찰계수 관계식이 자연하천 하상재료와 식생하천 식생자료의 유량과 마찰경사를 함수로 하는 멱함수 형의 식으로 각각 유도되었다. This study is used to analyze the distribution of resistance factors and the relationships of flow resistance with the field measurements which consist of the total 2,604 rivers for 1,865 bed material in natural channels and 739 vegetation in vegetated channels. Resistance fa
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