Donghwan Lee
KAIST 전기 및 전자공학부 · 컴퓨터과학
Donghwan Lee 교수의 연구실은 제어 이론과 최적화 이론을 기반으로 한 혁신적인 제어 알고리즘 및 최적화 기법을 개발하고 있습니다. 주요 연구 방향은 강화학습의 이론적 기반 강화, 비선형 시스템을 위한 모델 예측 제어(MPC), 빌리너 매트릭스 부등식(BMI) 기반 최적화, 스위치드 선형 시스템의 안정성 분석 등입니다. 특히 Q-학습의 수렴성 분석과 제어 리아프노프 함수의 새로운 형태를 도입하여 보다 효율적이고 안정적인 제어 알고리즘 설계에 기여하고 있습니다. 연구는 이론적 분석과 수치적 검증을 병행하여 실제 응용 가능성을 확보하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Alkenes and alcohols are among the most abundant and commonly used organic feedstock in industrial processes. We report a selective catalytic alkylation reaction of alkenes with alcohols that forms a carbon-carbon bond between vinyl carbon-hydrogen (C-H) and carbon-hydroxy centers with the concomitant loss of water. The cationic ruthenium complex [(C(6)H(6))(PCy(3))(CO)RuH](+)BF(4)(-) (Cy, cyclohexyl) catalyzes the alkylation in solution within 2 to 8 hours at temperatures ranging from 75° to 11
The associations of Internet addiction (IA) and smartphone addiction (SA) with mental health problems have been widely studied. We investigated the effects of IA and SA on depression and anxiety while adjusting for sociodemographic variables. In this study, 4854 participants completed a cross-sectional web-based survey including socio-demographic items, the Korean Scale for Internet Addiction, the Smartphone Addiction Proneness Scale, and the subscales of the Symptom Checklist 90 Items-Revised.
We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameter
As the feature size of NAND flash memory decreases, the threshold voltage signal becomes less reliable, and its distribution varies significantly with the number of program-erase (PE) cycles and the data retention time. We have developed parameter estimation algorithms to find the means and variances of the threshold voltage distribution that is modeled as a Gaussian mixture. The proposed methods find the best-fit parameters by minimizing the squared Euclidean distance between the measured thres
Abstract A mesoporous LTA zeolite (MP‐LTA)‐supported palladium catalyst was developed for the highly efficient Suzuki–Miyaura reaction of aryl and heteroaryl chlorides. The couplings of various aryl chlorides with arylboronic acids in aqueous ethanol were efficiently achieved in the presence of 1.0 mol% of the catalyst. Furthermore, the scope of this catalyst was extended to the coupling of heteroaryl chlorides. Regardless of the substituents, all of the coupling reactions were very clean and hi
Recently, reinforcement learning (RL) is receiving more and more attentions due to its successful demonstrations outperforming human performance in certain challenging tasks. The goal of this paper is to study a new optimization formulation of the linear quadratic regulator (LQR) problem via the Lagrangian duality theories in order to lay theoretical foundations of potentially effective RL algorithms. The new optimization problem includes the Q-function parameters so that it can be directly used
The new method has better performance than several existing methods, particularly in the estimation of the loading vectors.
A new β-diketiminatophosphane Pd catalyst was found to be highly effective in the mono and double Hiyama coupling reactions of unactivated aryl chlorides in water.
High-density NAND flash memory suffers from the data retention problem because even small charge leakage incurs a large threshold voltage shift as the transistor size shrinks. In this paper, we develop a decision directed estimation (DDE) algorithm to know the effects of charge leakage in NAND flash memory using the error pattern of the accessed data. While the conventional sensing directed estimation (SDE) method demands extra memory sensing to know the signal distribution, the proposed DDE alg
In this paper, a new linear matrix inequality-based model predictive control (MPC) problem is studied for discrete-time nonlinear systems described as Takagi-Sugeno fuzzy systems. A recent local stability approach is applied to improve the performance of the proposed MPC scheme. At each time k , an optimal state-feedback gain that minimizes an objective function is obtained by solving a semidefinite programming problem. The local stability analysis, the estimation of the domain of attraction, an
