Sungwon Kim

发表

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Anurag Malik, Anil Kumar, Sungwon Kim, 2020, Engineering Applications of Computational Fluid Mechanics.

Evaluation of pan evaporation modeling with two different neural networks and weather station data

Sungwon Kim, Vijay P. Singh, Youngmin Seo, 2014, Theoretical and Applied Climatology.

Neural Networks Model for Analysis of Input Information Uncertainty in the Small Catchment

Sungwon Kim, 2004 .

Derivation of the Optimal Artificial Neural Networks Model using Uncertainty Analysis

Sungwon Kim, 2007 .

Modeling Nonlinear Monthly Evapotranspiration Using Soft Computing and Data Reconstruction Techniques

Sungwon Kim, Hung Soo Kim, Vijay P. Singh, 2013, Water Resources Management.

Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods

Sungwon Kim, Zahra Zahmatkesh, Mohammad Rezaie-Balf, 2017, Water Resources Management.

The viability of co-active fuzzy inference system model for monthly reference evapotranspiration estimation: case study of Uttarakhand State

Anurag Malik, Anil Kumar, Sungwon Kim, 2019, Hydrology Research.

Construction of the FFWS using Supervised and Unsupervised Performance in the Small Catchment

Sungwon Kim, Ki-Bum Park, 2006 .

Continuous monitoring of suspended sediment concentrations using image analytics and deriving inherent correlations by machine learning

Sungwon Kim, Mohammad Ali Ghorbani, Rahman Khatibi, 2020, Scientific Reports.

A New Optimization Approach for the Least-Cost Design of Water Distribution Networks: Improved Crow Search Algorithm

Sungwon Kim, Mohammad Rezaie-Balf, Ozgur Kisi, 2019, Water Resources Management.

10nm high performance mobile SoC design and technology co-developed for performance, power, and area scaling

Sam Yang, Sungwon Kim, John Zhu, 2017, 2017 Symposium on VLSI Technology.

Generative Adversarial Networks for Crystal Structure Prediction

Sungwon Kim, Yousung Jung, Juhwan Noh, 2020, ACS central science.

Implementation on the evolutionary machine learning approaches for streamflow forecasting: case study in the Seybous River, Algeria

Sungwon Kim, Vijay P. Singh, Mousaab Zakhrouf, 2020 .

Lamb Wave Mode Decomposition Based on Cross-Wigner-Ville Distribution and Its Application to Anomaly Imaging for Structural Health Monitoring

Sungwon Kim, Daniel O. Adams, Ahmad B. Zoubi, 2019, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

River Stage Forecasting Using Wavelet Packet Decomposition and Data-driven Models

Sungwon Kim, Youngmin Seo, Sungwon Kim, 2016 .

Modeling of Rainfall by Combining Neural Computation and Wavelet Technique

Sungwon Kim, Chang-Joon Lee, Youngmin Seo, 2016 .

Uncertainty Reduction of the Flood Stage Forecasting Using Neural Networks Model1

Sungwon Kim, Hung Soo Kim, 2008 .

Nonlinear Analysis using the Artificial Neural Networks Model

Sungwon Kim, 2007 .

Machine-enabled inverse design of inorganic solid materials: promises and challenges

Sungwon Kim, Yousung Jung, Juhwan Noh, 2020, Chemical science.

Efficient storage and processing of large guided wave data sets with random projections

Alexander C. S. Douglass, Joel B. Harley, Sungwon Kim, 2020, Structural Health Monitoring.

Solar Radiation Estimation in Mediterranean Climate by Weather Variables Using a Novel Bayesian Model Averaging and Machine Learning Methods

Özgür Kisi, Sungwon Kim, Jalal Shiri, 2020, Neural Processing Letters.

Assessment of Pan Evaporation Modeling Using Bootstrap Resampling and Soft Computing Methods

Sungwon Kim, Vijay P. Singh, Youngmin Seo, 2015, J. Comput. Civ. Eng..

Super-Diffusive Behavior in Human Mobility and Finding Relevant Models in Mobile Opportunistic Networks.

Sungwon Kim, 2010 .

Standard Section Design of FRP DSCT Tower Supporting 3MW Wind Turbine

Sungwon Kim, Taek Hee Han, Hyemin Hong, 2016 .

Neural Networks-Genetic Algorithm Model for Modeling of Nonlinear Evaporation and Evapotranpiration Time Series. 2. Optimal Model Construction by Uncertainty Analysis

Sungwon Kim, Hung-Soo Kim, 2007 .

High Performance Mobile SoC Productization with Second-Generation 10-nm FinFET Technology and Extension to 8-nm Scaling

Sam Yang, Sungwon Kim, Kwon Lee, 2018, 2018 IEEE Symposium on VLSI Technology.

5G and AI Integrated High Performance Mobile SoC Process-Design Co-Development and Production with 7nm EUV FinFET Technology

Sungwon Kim, Hao Wang, Lixin Ge, 2020, 2020 IEEE Symposium on VLSI Technology.

Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search pdf

Sungwon Kim, Sungroh Yoon, Jaehyeon Kim, 2020, NeurIPS.

Multistep-ahead flood forecasting using wavelet and data-driven methods

Sungwon Kim, Vijay P. Singh, Youngmin Seo, 2015, KSCE Journal of Civil Engineering.

Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals

Sungwon Kim, Juhwan Noh, Geun Ho Gu, 2020, J. Chem. Inf. Model..

Optimization of Reservoir Operation using New Hybrid Algorithm

Sungwon Kim, Zaher Mundher Yaseen, Ozgur Kisi, 2018 .

Novel hybrid approaches based on evolutionary strategy for streamflow forecasting in the Chellif River, Algeria

Sungwon Kim, Mousaab Zakhrouf, Madani Stamboul, 2019, Acta Geophysica.

Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)

Sungwon Kim, Salim Heddam, Mousaab Zakhrouf, 2018 .

Can Decomposition Approaches Always Enhance Soft Computing Models? Predicting the Dissolved Oxygen Concentration in the St. Johns River, Florida

Sungwon Kim, Mohammad Ali Ghorbani, Vijay P. Singh, 2019, Applied Sciences.

Current Usage and Proliferation of Library 2.0 from User Viewpoint: Focusing on Folksonomy

Sungwon Kim, Jeong-Woo Kim, Jeong-woo Kim, 2013 .

Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia

Sungwon Kim, Mohammad Ali Ghorbani, Ravinesh C. Deo, 2020, Soft Comput..

FloWaveNet : A Generative Flow for Raw Audio pdf

Sungwon Kim, Sungroh Yoon, Sang-gil Lee, 2018, ICML.

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

Sungwon Kim, Mohammad Rezaie-Balf, Nam Won Kim, 2019, Energies.

Daily water level forecasting using wavelet decomposition and artificial intelligence techniques

Sungwon Kim, Ozgur Kisi, Vijay P. Singh, 2015 .

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Sungwon Kim, Vijay P. Singh, Youngmin Seo, 2018, Atmosphere.

Modeling the physical dynamics of daily dew point temperature using soft computing techniques

Sungwon Kim, Chang-Joon Lee, Vijay P. Singh, 2015 .

Flood Forecasting Using Neural Computing Techniques and Conceptual Class Segregation

Sungwon Kim, Vijay P. Singh, Sungwon Kim, 2013 .

Estimation of Long-Term Monthly Temperatures by Three Different Adaptive Neuro-Fuzzy Approaches Using Geographical Inputs

Sungwon Kim, Ozgur Kisi, Vahdettin Demir, 2017 .

Neural networks model and embedded stochastic processes for hydrological analysis in South Korea

Sungwon Kim, Sungwon Kim, 2004 .

River Stage Forecasting Using Wavelet Packet Decomposition and Machine Learning Models

Sungwon Kim, Ozgur Kisi, Vijay P. Singh, 2016, Water Resources Management.

Estimating Global Solar Irradiance for Optimal Photovoltaic System

Sungwon Kim, Vijay P. Singh, Youngmin Seo, 2016 .

Modeling of Solar Radiation Using Artificial Neural Network

Sungwon Kim, Sungwon Kim, 2014 .

NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity pdf

Sungwon Kim, Sungroh Yoon, Sang-gil Lee, 2020, NeurIPS.

Modelling daily reference evapotranspiration in humid locations of South Korea using local and cross‐station data management scenarios

Sungwon Kim, Ozgur Kisi, Jalal Shiri, 2017 .

Integrational Operation Method using Stochastic/Neural Networks Models

Sungwon Kim, Hung Soo Kim, H. Kim, 2008 .

Deep echo state network: a novel machine learning approach to model dew point temperature using meteorological variables

Sungwon Kim, Ozgur Kisi, Meysam Alizamir, 2020 .

Hydrological Forecasting Using Hybrid Data-Driven Approach

Sungwon Kim, Youngmin Seo, Sungwon Kim, 2016 .