Jongsoo Lee
Yonsei University
AlgorithmMathematical optimizationEngineeringFinite element methodArtificial intelligenceOptimal designMaterials scienceConstraint (information theory)Context (language use)MathematicsDesign of experimentsEngineering design processComputer scienceReliability (statistics)Artificial neural networkControl theoryStructural engineeringMulti-objective optimizationMedicineMechanical engineeringGenetic algorithm
168Publications
17H-index
1,047Citations
Publications 165
Newest
#1Hyeongwoon Lee (Yonsei University)
#2Jongsoo Lee (Yonsei University)H-Index: 17
Abstract This study proposes a novel deep learning methodology to evaluate the interior noise in vehicles on mechanical and affective levels by employing small data sets. A convolutional neural network (CNN) model is constructed from the frequency-rpm spectrograms of vehicle noises to predict the mechanical attributes of the noise. The noises are classified based on the number of engine cylinders (3, 4, 6, and 8). Owing to the high variability in spectrograms, mathematical expressions for the en...
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#1Jee Soo Park (Yonsei University)H-Index: 8
#2Myung Eun Lee (Yonsei University)H-Index: 3
Last. Won Sik Ham (Yonsei University)H-Index: 21
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Genes associated with the DEAD-box helicase DDX11 are significant biomarkers of aggressive renal cell carcinoma (RCC), but their molecular function is poorly understood. We analyzed the molecular pathways through which DDX11 is involved in RCC cell survival and poly (ADP-ribose) polymerase (PARP) inhibitor sensitivity. Immunohistochemistry and immunoblotting determined DDX11 expression in normal kidney tissues, benign renal tumors, and RCC tissues and cell lines. Quantitative polymerase chain re...
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#1Chan-mo Kang (Electronics and Telecommunications Research Institute)H-Index: 12
#2Jin-Wook Shin (Electronics and Telecommunications Research Institute)H-Index: 17
Last. Soon-gi ParkH-Index: 1
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#1Na Young Kim (Yonsei University)H-Index: 11
#2Dongwoo Chae (Yonsei University)H-Index: 6
Last. So Yeon Kim (Yonsei University)H-Index: 23
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BACKGROUND: Acute kidney injury after partial or radical nephrectomy remains an unsolved problem even when using minimally invasive techniques. We aimed to identify risk factors for acute kidney injury (AKI) after minimally invasive nephrectomy and to develop a clinical risk scoring system. METHODS: Medical records of 1762 patients who underwent minimally invasive laparoscopic or robot-assisted laparoscopic partial (n = 1009) or radical (n = 753) nephrectomy from December 2005 to November 2018 w...
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#1Youngjun Kim (Yonsei University)H-Index: 12
#2Jongsoo Lee (Yonsei University)H-Index: 17
Uncertainties cause tremendous failures, especially in large-scale system design, because they are accumulated from each of the subsystems. Analytical target cascading is a multidisciplinary design...
1 CitationsSource
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#1Chan-mo Kang (Electronics and Telecommunications Research Institute)H-Index: 12
#2Jin-Wook Shin (Electronics and Telecommunications Research Institute)H-Index: 17
Last. Chun-Won Byun (Electronics and Telecommunications Research Institute)H-Index: 18
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Organic light-emitting diode (OLED) microdisplays have attracted much attention as displays for small form factor augmented reality (AR) devices. To realize glass-like thin and wide field of view (...
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#1Periklis Koukourikis (Yonsei University)H-Index: 1
#2Ali Abdullah Alqahtani (Yonsei University)H-Index: 1
Last. Koon Ho Rha (Yonsei University)H-Index: 51
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OBJECTIVES To evaluate the safety and efficacy, and long-term functional and oncologic outcomes of robot-assisted partial nephrectomy in high-complexity tumors. METHODS Data of 155 patients with a high-complexity tumor (PADUA score ≥10) were reviewed. Trifecta achievement, intra-, perioperative, functional, and oncologic outcomes were analyzed and compared between patients with increasing complexity. RESULTS Of the 155 patients, 65 (41.9%) patients had a PADUA score of 10, 55 (35.5%) had a PADUA...
1 CitationsSource
#1Yeongmin Yoo (Yonsei University)H-Index: 1
#2Ui-Jin Jung (Hyundai Motor Group)
Last. Jongsoo Lee (Yonsei University)H-Index: 17
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Abstract Owing to uncertainty factors present in the system, computer-aided engineering (CAE) models suffer from limitations in terms of accuracy of test model representation. This paper proposes a new predictive model, termed designable generative adversarial network (DGAN), which applies the Inverse generator neural network to GAN, one of the methods employed for data augmentation. Statistical model-based validation and calibration technology, employed for improving the accuracy of a predictiv...
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