Won Chang
Seoul National University · 地球惑星科学
研究室紹介
Professor Won Chang's research spans interdisciplinary areas at the intersection of economics, materials science, and biomedical imaging. His lab investigates the economic impacts of regional trade agreements using game-theoretic models and empirical trade data, while also exploring quantum phenomena in low-dimensional semiconductor nanostructures through advanced spectroscopic techniques. Additionally, the lab contributes to medical imaging innovation, particularly in optimizing MRI-based elastography for non-invasive liver fibrosis assessment. The work integrates quantitative modeling, experimental physics, and clinical applications to address challenges in global trade, quantum materials, and precision medicine.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15The welfare effects of PTAs are most directly linked to changes in trade prices, i.e., the terms of trade. This paper employs a simple strategic pricing game in segmented markets to measure the effects of MERCOSUR on the pricing of “nonmember” exports to Brazil: As Brazil exempts its MERCOSUR partners from tariffs, the resulting competitive pressure leads other exporters to reduce their prices. Working with detailed data on unit values and tariffs we find that the creation of MERCOSUR was associ
The spectrum of a segment of InAs nanowire, confined between two superconducting leads, was measured as function of gate voltage and superconducting phase difference using a third normal-metal tunnel probe. Subgap resonances for odd electron occupancy-interpreted as bound states involving a confined electron and a quasiparticle from the superconducting leads, reminiscent of Yu-Shiba-Rusinov states-evolve into Kondo-related resonances at higher magnetic fields. An additional zero-bias peak of unk
Abstract. A 3-D hybrid ice-sheet model is applied to the last deglacial retreat of the West Antarctic Ice Sheet over the last ∼ 20 000 yr. A large ensemble of 625 model runs is used to calibrate the model to modern and geologic data, including reconstructed grounding lines, relative sea-level records, elevation–age data and uplift rates, with an aggregate score computed for each run that measures overall model–data misfit. Two types of statistical methods are used to analyze the large-ensemble r
Abstract Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6%–7% K −1 , following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (1%–2% K −1 in the global average in transient runs). Some other aspect of precipitation events must then change to compensate for this difference. The authors d
Rapid retreat of ice in the Amundsen Sea sector of West Antarctica may cause drastic sea level rise, posing significant risks to populations in low-lying coastal regions. Calibration of computer models representing the behavior of the West Antarctic Ice Sheet is key for informative projections of future sea level rise. However, both the relevant observations and the model output are high-dimensional binary spatial data; existing computer model calibration methods are unable to handle such data.
Abstract This paper presents a model reduction method and uncertainty modeling for the design of a low-order H∞ robust controller for suppression of smart panel vibration. A smart panel with collocated piezoceramic actuators and sensors is modeled using solid, transition, and shell finite elements, and then the size of the model is reduced in the state space domain. A robust controller is designed not only to minimize the panel vibration excited by applied uniform acoustic pressure, but also to
Data-driven machine learning approaches have been rapidly developed in the past 10 to 20 years and applied to various problems in the field of hydrology. To investigate the capability of data-driven approaches in rainfall-runoff modeling in comparison to theory-driven models, we conducted a comparative study of simulated monthly surface runoff at 203 watersheds across the contiguous USA using a conceptual model, the proportionality hydrologic model, and a data-driven Gaussian process regression
We propose a decode-and-forward (DF) strategy for a relay channel with two half-duplex relays that alternately forward their received messages. The proposed strategy is to combine two distinctive schemes which differently treat a codeword that each relay receives from the other relay. One lets each relay consider it as interference, while the other makes each relay decode and forward it to the destination. We show the channel conditions under which each scheme outperforms the other and combining
Abstract. Computer models of ice sheet behavior are important tools for projecting future sea level rise. The simulated modern ice sheets generated by these models differ markedly as input parameters are varied. To ensure accurate ice sheet mass loss projections, these parameters must be constrained using observational data. Which model parameter combinations make sense, given observations? Our method assigns probabilities to parameter combinations based on how well the model reproduces the Gree
A milking operation simulation model was developed using an object-oriented approach. Six object classes were defined: things, living things, animals, operator, cow, and parlor. Operator, cow, and parlor classes were the basic units of the model. Each object in the model is a self-contained unit that represents its real world counterpart. The model operation and events were scheduled by sending messages among the objects. Messages caused operator and cows to move between locations inside the par
Sleep is a critical component of health and well-being but collecting and analyzing accurate longitudinal sleep data can be challenging, especially outside of laboratory settings. We propose a simple neural network model titled SOMNI (Sleep data restOration using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep-wake cycles of individuals with highly ir