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Minwoo Lee

Publications and source records attributed to Minwoo Lee.

43 records · Page 3Linked to original sources
arXiv2021

System Identification near a Hopf Bifurcation via the Noise-Induced Dynamics in the Fixed-Point Regime

A Hopf bifurcation is prevalent in many nonlinear dynamical systems. When a system prior to a Hopf bifurcation is exposed to a sufficient level of noise, its noise-induced dynamics can provide valuable information about the impending bifurcation. In this thesis, we present a system identification (SI) framework that exploits the noise-induced dynamics prior to a Hopf bifurcation. The framework is novel in that it is capable of predicting the bifurcation point and the post-bifurcation dynamics using only pre-bifurcation data. Specifically, we present two different versions of the framework: input-output and output-only. For the input-output version, the system is forced with additive noise generated by an external actuator, and its response is measured. For the output-only version, the intrinsic noise of the system acts as the noise source and only the output signal is measured. In both versions, the Fokker-Planck equations, which describe the probability density function of the fluctuation amplitude, are derived from self-excited oscillator models. Then, the coefficients of these models are extracted from the experimental probability density functions characterizing the noise-induced response in the fixed-point regime. The SI framework is tested on three different experimental systems: a low-density jet, a flame-driven Rijke tube, and a gas-turbine combustor. For these systems, we demonstrate that the proposed framework can identify the nature of the Hopf bifurcation and the system's order of nonlinearity. Moreover, by extrapolating the identified model coefficients, we are able to forecast the locations of the bifurcation points and the limit-cycle features after those points. To the best of our knowledge, this is the first time that SI has been performed using data from only the pre-bifurcation regime, without the need for knowledge of the location of the bifurcation point.

physics.flu-dyn↗
arXiv2021

Demystifying Deep Neural Networks Through Interpretation: A Survey

Modern deep learning algorithms tend to optimize an objective metric, such as minimize a cross entropy loss on a training dataset, to be able to learn. The problem is that the single metric is an incomplete description of the real world tasks. The single metric cannot explain why the algorithm learn. When an erroneous happens, the lack of interpretability causes a hardness of understanding and fixing the error. Recently, there are works done to tackle the problem of interpretability to provide insights into neural networks behavior and thought process. The works are important to identify potential bias and to ensure algorithm fairness as well as expected performance.

cs.LG↗
arXiv2020

Few-Shot Keyword Spotting With Prototypical Networks

Recognizing a particular command or a keyword, keyword spotting has been widely used in many voice interfaces such as Amazon's Alexa and Google Home. In order to recognize a set of keywords, most of the recent deep learning based approaches use a neural network trained with a large number of samples to identify certain pre-defined keywords. This restricts the system from recognizing new, user-defined keywords. Therefore, we first formulate this problem as a few-shot keyword spotting and approach it using metric learning. To enable this research, we also synthesize and publish a Few-shot Google Speech Commands dataset. We then propose a solution to the few-shot keyword spotting problem using temporal and dilated convolutions on prototypical networks. Our comparative experimental results demonstrate keyword spotting of new keywords using just a small number of samples.

eess.AS↗
arXiv2020

Drug-disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

Adverse Drug Reaction (ADR) is a significant public health concern world-wide. Numerous graph-based methods have been applied to biomedical graphs for predicting ADRs in pre-marketing phases. ADR detection in post-market surveillance is no less important than pre-marketing assessment, and ADR detection with large-scale clinical data have attracted much attention in recent years. However, there are not many studies considering graph structures from clinical data for detecting an ADR signal, which is a pair of a prescription and a diagnosis that might be a potential ADR. In this study, we develop a novel graph-based framework for ADR signal detection using healthcare claims data. We construct a Drug-disease graph with nodes representing the medical codes. The edges are given as the relationships between two codes, computed using the data. We apply Graph Neural Network to predict ADR signals, using labels from the Side Effect Resource database. The model shows improved AUROC and AUPRC performance of 0.795 and 0.775, compared to other algorithms, showing that it successfully learns node representations expressive of those relationships. Furthermore, our model predicts ADR pairs that do not exist in the established ADR database, showing its capability to supplement the ADR database.

cs.LG↗
arXiv2019

Machine learning approach to remove ion interference effect in agricultural nutrient solutions

High concentration agricultural facilities such as vertical farms or plant factories consider hydroponic techniques as optimal solutions. Although closed-system dramatically reduces water consumption and pollution issues, it has ion-ratio related problem. As the root absorbs individual ions with different rate, ion rate in a nutrient solution should be adjusted periodically. But traditional method only considers pH and electrical conductivity to adjust the nutrient solution, leading to ion imbalance and accumulation of excessive salts. To avoid those problems, some researchers have proposed ion-balancing methods which measure and control each ion concentration. However, those approaches do not overcome the innate limitations of ISEs, especially ion interference effect. An anion sensor is affected by other anions, and the error grows larger in higher concentration solution. A machine learning approach to modify ISE data distorted by ion interference effect is proposed in this paper. As measurement of TDS value is relatively robust than any other signals, we applied TDS as key parameter to build a readjustment function to remove the artifact. Once a readjustment model is established, application on ISE data can be done in real time. Readjusted data with proposed model showed about 91.6 ~ 98.3% accuracies. This method will enable the fields to apply recent methods in feasible status.

cs.LG↗
arXiv2019

ODE network model for nonlinear and complex agricultural nutrient solution system

In closed hydroponic systems, periodic readjustment of nutrient solution is necessary to continuously provide stable environment to plant roots because the interaction between plant and nutrient solution changes the rate of ions in it. The traditional method is to repeat supplying small amount of premade concentrated nutrient solution, measuring total electric conductivity and pH of the tank only. As it cannot control the collapse of ion rates, recent researches try to measure the concentration of individual components to provide insufficient ions only. However, those approaches use titrationlike heuristic approaches, which repeat adding small amount of components and measuring ion density a lot of times for a single control input. Both traditional and recent methods are not only time-consuming, but also cannot predict chemical reactions related with control inputs because the nutrient solution is a nonlinear complex system, including many precipitation reactions and complicated interactions. We present a continuous network model of the nutrient solution system, whose reactions are described as differential equations. The model predicts molar concentration of each chemical components and total dissolved solids with low error. This model also can calculate the amount of chemical compounds needed to produce a desired nutrient solution, by reverse calculation from dissolved ion concentrations.

eess.SY↗