143 citations · 429 across the 41 of their papers we have counts for
17 papers · 1 filter
SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization
Paul F. Lang, Sungho Shin, Victor M. Zavala
Motivation: Estimating model parameters from experimental observations is one of the key challenges in systems biology and can be computationally very expensive. While the Julia pr…
Graph-Based Modeling and Decomposition of Energy Infrastructures
Sungho Shin, Carleton Coffrin, Kaarthik Sundar +1
Nonlinear optimization problems are found at the heart of real-time operations of critical infrastructures. These problems are computationally challenging because they embed comple…
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks
Yoonho Boo, Sungho Shin, Jungwook Choi +1
The quantization of deep neural networks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve t…
Multiple Classification with Split Learning
Jongwon Kim, Sungho Shin, Yeonguk Yu +2
Privacy issues were raised in the process of training deep learning in medical, mobility, and other fields. To solve this problem, we present privacy-preserving distributed deep le…
S-SGD: Symmetrical Stochastic Gradient Descent with Weight Noise Injection for Reaching Flat Minima
Wonyong Sung, Iksoo Choi, Jinhwan Park +2
The stochastic gradient descent (SGD) method is most widely used for deep neural network (DNN) training. However, the method does not always converge to a flat minimum of the loss…
A Graph-Based Modeling Abstraction for Optimization: Concepts and Implementation in Plasmo.jl
Jordan Jalving, Sungho Shin, Victor M. Zavala
We present a general graph-based modeling abstraction for optimization that we call an OptiGraph. Under this abstraction, any optimization problem is treated as a hierarchical hype…
Quantized Neural Networks: Characterization and Holistic Optimization
Yoonho Boo, Sungho Shin, Wonyong Sung
Quantized deep neural networks (QDNNs) are necessary for low-power, high throughput, and embedded applications. Previous studies mostly focused on developing optimization methods f…
On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control
Sen Na, Sungho Shin, Mihai Anitescu +1
We study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domai…
Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control
Qiugang Lu, Sungho Shin, Victor M. Zavala
Dynamic mode decomposition (DMD) is a versatile approach that enables the construction of low-order models from data. Controller design tasks based on such models require estimates…
Unifying Theorems for Subspace Identification and Dynamic Mode Decomposition
Sungho Shin, Qiugang Lu, Victor M. Zavala
This paper presents unifying results for subspace identification (SID) and dynamic mode decomposition (DMD) for autonomous dynamical systems. We observe that SID seeks to solve an…
On the Convergence of the Dynamic Inner PCA Algorithm
Sungho Shin, Alex D. Smith, S. Joe Qin +1
Dynamic inner principal component analysis (DiPCA) is a powerful method for the analysis of time-dependent multivariate data. DiPCA extracts dynamic latent variables that capture t…
Overlapping Schwarz Decomposition for Constrained Quadratic Programs
Sungho Shin, Mihai Anitescu, Victor M. Zavala
We present an overlapping Schwarz decomposition algorithm for constrained quadratic programs (QPs). Schwarz algorithms have been traditionally used to solve linear algebra systems…
Multi-Grid Schemes for Multi-Scale Coordination of Energy Systems
Sungho Shin, Victor M. Zavala
We discuss how multi-grid computing schemes can be used to design hierarchical coordination architectures for energy systems. These hierarchical architectures can be used to manage…
Computing Economic-Optimal and Stable Equilibria for Droop-Controlled Microgrids
Sungho Shin, Victor M. Zavala
We consider the problem of computing equilibria (steady-states) for droop-controlled, islanded, AC microgrids that are both economic-optimal and dynamically stable. This work is mo…
A Hierarchical Optimization Architecture for Large-Scale Power Networks
Sungho Shin, Philip Hart, Thomas Jahns +1
We present a hierarchical optimization architecture for large-scale power networks that overcomes limitations of fully centralized and fully decentralized architectures. The archit…
SQWA: Stochastic Quantized Weight Averaging for Improving the Generalization Capability of Low-Precision Deep Neural Networks
Sungho Shin, Yoonho Boo, Wonyong Sung
Designing a deep neural network (DNN) with good generalization capability is a complex process especially when the weights are severely quantized. Model averaging is a promising ap…
Diffusing-Horizon Model Predictive Control
Sungho Shin, Victor M. Zavala
We analyze a time-coarsening strategy for model predictive control (MPC) that we call diffusing-horizon MPC. This strategy seeks to overcome the computational challenges associated…