Jinsoo Bae

Jaehoon Kim

Seokho Moon

Jinhyeok Park

Chunghyup Mok

Yongwon Jo

Youngjae Lee

Insung Baek

Jiyoon Lee

Minjung Lee

Wooksoo Shin

Yoon Sang Cho

Mingu Kwak

Hyungu Kahng

Hyungrok Do

Youngjoon Park

Hankyu Lee

Sangmin Lee

Younghoon Kim

Sungho Park

Seulki Lee

Jaehong Yu

Jieun Son

Chanhee Park

Sugon Cho

Jihoon Kang

Tuerhong Gulanbaier

Poovich Phaladiganon

Panitarn Chongfuangprinya

Thee Jitpitaklert

Panaya Rattakorn

Thuntee Sukchotrat

Chivalai Temiyasathit

Dachuan Shih

Kwangeun Cho

Yongsu Jo

Kyungseok Han

Junbeom Lee

Jihyun Yun

Changwook Chu

Minsung Cha

Jongwon Choi

Yongtae Jeong

Younghwan Lee

Taenam Park

Minji Baek

Jinwoo Lee

Gunhui Jang

Jonghyun Lee

Goeun Chae

Hyeonji Kim

Byeongeun Ko

Kyungsoo Kim

Minjae Baek

Changhyun Kim

Jaeyoon Jeong

Hyeryeong Oh

Eunsung Ko

Leekyung Yoo

Sangmin Kim

Taeyeon Kim

Eunji Koh

Seongjin Hwang

Seokcheol Hwang

Inbum Ahn

Jeongwon Kim

Kyungseon Cho

Seoyeon Kim

Keewon Jeong

Haeun Hwang

Minjae Lee

Woosik Choi

Dabin Min

Seungho Back

Hyeongyu Kang

Seungsup Chung

Changhyun Lee

Yooyeon Sung

Gonie Ahn

Sunyool Chae

Youngjae Jung

Seonghyeon Kang

Hwayun Yu

Wansik Nam

Jinsoo Park

Jayeon Gu

Hyunjin So

TaewooJoo

Kyujong Lee

Junghoon Kim

Junghwan Son

Sinyoung Kim

Mr. Carlos Duran

Dongkyo Lee

Dongju Kim

Jisun Park

Yongwon Yoo

Hyunyoung Jung

Jaeyong Shin

Data Mining & Quality Analytics Lab. Our lab is dedicated to research with passion for Data Mining/Artificial Intelligence/Quality. We will try to conduct meaningful research to help society. 본 연구실 홈페이지는 "내부구성원들의 발자취 기록" 과 "지식의 나눔" 을 목적으로 운영되고 있습니다. 따라서 연구실에서 행해지는 크고 작은 이벤트들과 연구내용을 비롯한 많은 유용한 자료들을 공개하여 여러분들과 공유할 예정입니다. 본 연구실 홈페이지가 머신러닝/인공지능 분야에 관심 있는 모든 분들에게 도움이 될 수 있도록 노력하겠습니다.

본 연구실 홈페이지는 "내부구성원들의 발자취 기록" 과 "지식의 나눔" 을 목적으로 운영되고 있습니다. 따라서 연구실에서 행해지는 크고 작은 이벤트들과 연구내용을 비롯한 많은 유용한 자료들을 공개하여 여러분들과 공유할 예정입니다. 본 연구실 홈페이지가 머신러닝/인공지능 분야에 관심 있는 모든 분들에게 도움이 될 수 있도록 노력하겠습니다.

Policy likelihood-based query sampling and critic-exploited reset for efficient preference-based reinforcement learning

CATS-RAG: Contextual augmented triplet synthesis for RAG in technical QA

Semi-supervised learning under extreme class imbalance in wafer bin map defect pattern classification

Sampling frequency-aware transformer specialized for unsupervised anomaly detection in irregular multivariate time series

Accuracy-informed label smoothing and logit scaling for deep neural network calibration

Deep
reinforcement
learning
for
scheduling
semiconductor
cluster
tools
in
varying
configurations

D-QMIX: Multi-step sequential forward dynamics modeling with global state and self-attention for sample-efficient multi-agent reinforcement learning

CFAST simulations and application of a machine learning algorithm for the fire safety of a switchgear room

Explainable machine learning-based meta-modeling for predicting fire damage to motor control cabinets in a switchgear room

Multi-expert distributionally robust optimization for out-of-distribution generalization

CaliMatch: Adaptive calibration for improving safe semi-supervised learning

Data-driven component-level decision-making for online remanufacturing of gas-insulated switchgear

Variable-guided attention for multichannel signal anomaly detection

Robust semantic segmentation of wafer  image using multi-task learning with edge detection

Uncertainty-informed dynamic threshold for time series anomaly detection

Safe
semi-supervised
contrastive
learning
using
in-distribution
data
as
positive
examples

Virtual try-on with pose-aware diffusion models

Self-supervised learning for vehicle noise prediction with limited labeled data

Self-supervised spatiotemporal representation learning for skeleton-based human action recognition

Masked and inverse dynamics modeling for data-efficient reinforcement learning

FALL: prior failure detection in large scale system based on language model

Semi-supervised learning with wafer-specific augmentations for wafer defect classification

Corruption-based anomaly detection and interpretation in tabular data

Input-guidance diffusion model for unknown defect patterns detection in wafer bin map

Mixing corrupted preferences for robust and feedback-efficient preference-based reinforcement learning

Multi-source partial domain adaptation with Gaussian-based dual-level weighting for PPG-based heart rate estimation

Weakly supervised image segmentation for detecting defects from scanning electron microscopy images in semiconductor

Contrastive learning with hard negative samples for chest X-ray multi-label classification

GraphixMatch: Improving semi-supervised learning for graph classification with FixMatch

DynaSTI: Dynamics modeling with sequential temporal information for reinforcement learning in Atari

Self- and semi-supervised learning for evacuation time modeling within fire emergencies in nuclear power plants

Semantic segmentation for noisy and limited wafer transmission electron microscope images

Cross-subject generalizable representation learning with class-subject dual labels for biosignals

Improving webtoon accessibility for color vision deficiency in South Korea using deep learning

One-shot structure-aware stylized image synthesis

Compose and conquer: diffusion-based 3D depth aware composable image synthesis

Noise map guidance: inversion with spatial context for real image editing

Identification of primary input parameters affecting evacuation in ventilated main control room through CFAST simulations and applicati

언어 모델에 기초하여 대규모 시스템에서 조기 오류를 탐지하는 방법

자기 지도 학습과 결합된 멀티 에이전트 강화학습을 수행하는 방법 및 그 장치

치안 및 공공 데이터 기반 범죄 신고 수 예측 장치 및 방법

이종 스펙트럼 기반 혈당 추정 장치 및 방법

Content-based filtering for recommendation systems using multiattribute networks

A review and analysis of the Mahalanobis—Taguchi system

Process monitoring using variational autoencoder for high-dimensional nonlinear processes

One-class classification-based control charts for multivariate process monitoring

Intelligent traffic control for autonomous vehicle systems based on machine learning

An overlap-sensitive margin classifier for imbalanced and overlapping data

Time series forecasting based on wavelet filtering

Academic paper recommender system using multilevel simultaneous citation networks

Bootstrap-Based T 2 Multivariate Control Charts

A hybrid approach of goal programming for weapon systems selection

Sequential random k-nearest neighbor feature selection for high-dimensional data

Review and analysis of recommender systems

Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra

Parallel simulated annealing with a greedy algorithm for Bayesian network structure learning

Principal component analysis-based control charts for multivariate nonnormal distributions

A data-integrated simulation model to evaluate nurse–patient assignments

A comparison of CUSUM, EWMA, and temporal scan statistics for detection of increases in Poisson rates

Finding meaningful pattern of key words in IIE transactions using text mining

Association rule mining and network analysis in oriental medicine

Gower distance-based multivariate control charts for a mixture of continuous and categorical variables

Seoung Bum Kim

Content-based filtering (CBF), one of the most successful recommendation techniques, is based on correlations between contents. CBF uses item information, represented as attributes, to calculate the similarities between items. In this study, we propose a novel CBF method that uses a multiattribute network to effectively reflect several attributes when calculating correlations to recommend items to users. In the network analysis, we measure the similarities between directly and indirectly linked items. Moreover, our proposed method employs centrality and clustering techniques to consider the mutual relationships among items, as well as determine the structural patterns of these interactions. This mechanism ensures that a variety of items are recommended to the user, which improves the performance. We compared the proposed approach with existing approaches using MovieLens data, and found that our approach …

The Mahalanobis–Taguchi system (MTS) is a relatively new collection of methods proposed for diagnosis and forecasting using multivariate data. The primary proponent of the MTS is Genichi Taguchi, who is very well known for his controversial ideas and methods for using designed experiments. The MTS results in a Mahalanobis distance scale used to measure the level of abnormality of “abnormal” items compared to a group of “normal” items. First, it must be demonstrated that a Mahalanobis distance measure based on all available variables on the items is able to separate the abnormal items from the normal items. If this is the case, then orthogonal arrays and signal-to-noise ratios are used to select an “optimal” combination of variables for calculating the Mahalanobis distances. Optimality is defined in terms of the ability of the Mahalanobis distance scale to match a prespecified or estimated scale that measures …

In many industries, statistical process monitoring techniques play a key role in improving processes through variation reduction and defect prevention. Modern large-scale industrial processes require appropriate monitoring techniques that can efficiently address high-dimensional nonlinear processes. Such processes have been successfully monitored with several latent variable-based methods. However, because these monitoring methods use Hotelling’s T 2 statistics in the reduced space, a normality assumption underlies the construction of these tools. This assumption has limited the use of latent variable-based monitoring charts in both nonlinear and nonnormal situations. In this study, we propose a variational autoencoder (VAE) as a monitoring method that can address both nonlinear and nonnormal situations in high-dimensional processes. VAE is appropriate for T 2 charts because it causes the reduced …

One-class classification problems have attracted a great deal of attention from various disciplines. In the present study, attempts are made to extend the scope of application of the one-class classification technique to Statistical Process Control (SPC) problems. New multivariate control charts that apply the effectiveness of one-class classification to improvement of Phase I and Phase II analysis in SPC are proposed. These charts use a monitoring statistic to represent the degree of being an outlier as obtained through one-class classification. The control limits of the proposed charts are established based on the empirical level of significance on the percentile, estimated by the bootstrap method. A simulation study is conducted to illustrate the limitations of current one-class classification control charts and demonstrate the effectiveness of the proposed control charts.

This study aimed to resolve a real-world traffic problem in a large-scale plant. Autonomous vehicle systems (AVSs), which are designed to use multiple vehicles to transfer materials, are widely used to transfer wafers in semiconductor manufacturing. Traffic control is a significant challenge with AVSs because all vehicles must be monitored and controlled in real time, to cope with uncertainties such as congestion. However, existing traffic control systems, which are primarily designed and controlled by human experts, are insufficient to prevent heavy congestion that impedes production. In this study, we developed a traffic control system based on machine learning predictions, and a routing method that dynamically determines AVS routes with reduced congestion rates. We predicted congestion for critical bottleneck areas, and utilized the predictions for adaptive routing control of all vehicles to avoid congestion. We …

Classification is an important task in various areas. In many real-world applications, class imbalance and overlapping problems have been reported as major issues in the application of traditional classification algorithms. An imbalance problem occurs when training data contain considerably more representatives of one class than of other classes. Class overlap occurs when a region in the data space contains a similar number of data for each class. When a class overlap occurs in imbalanced data sets, classification becomes even more complicated. Although various approaches have been proposed to deal separately with class imbalance and overlapping problems, only a few studies have attempted to address both problems simultaneously. In this paper, we propose an overlap-sensitive margin (OSM) classifier based on a modified fuzzy support vector machine and k -nearest neighbor algorithm to address …

Forecasting time series data is one of the most important issues involved in numerous applications in real life. Time series data have been analyzed in either the time or frequency domains. The objective of this study is to propose a forecasting method based on wavelet filtering. The proposed method decomposes the original time series into the trend and variation parts and constructs a separate model for each part. Simulation and real case studies were conducted to examine the properties of the proposed method under various scenarios and compare its performance with time series forecasting models without wavelet filtering. The results from both simulated and real data showed that the proposed method based on wavelet filtering yielded more accurate results than the models without wavelet filtering in terms of mean absolute percentage error criterion.

Researchers typically need to filter several academic papers to find those relevant to their research. This filtering is cumbersome and time-consuming because the number of published academic papers is growing exponentially. Some researchers have focused on developing better recommender systems for academic papers by using citation analysis and content analysis. Most traditional content analysis is implemented using a keyword matching process, and thus it cannot consider the semantic contexts of items. Further, citation analysis-based techniques rely on the number of links directly citing or being cited in a single-level network. Consequently, it may be difficult to recommend the appropriate papers when the paper of interest does not have enough citation information. To address these problems, we propose a recommendation system for academic papers that combines citation analysis and network …

Control charts have been used effectively for years to monitor processes and detect abnormal behaviors. However, most control charts require a specific distribution to establish their control limits. The bootstrap method is a nonparametric technique that does not rely on the assumption of a parametric distribution of the observed data. Although the bootstrap technique has been used to develop univariate control charts to monitor a single process, no effort has been made to integrate the effectiveness of the bootstrap technique with multivariate control charts. In the present study, we propose a bootstrap-based multivariate T 2 control chart that can efficiently monitor a process when the distribution of observed data is nonnormal or unknown. A simulation study was conducted to evaluate the performance of the proposed control chart and compare it with a traditional Hotelling's T 2 control chart and the kernel density …

Because weapon systems are perceived as crucial in determining the outcome of a war, selecting weapon systems is a critical task for nations. Just as with other forms of decision analysis involving multiple criteria, selecting a weapon system poses complex, unstructured problems with a huge number of points that must be considered. Some defense analysts have committed themselves to developing efficient methodologies to solve weapon systems selection problems for the Republic of Korea’s (ROK) Armed Forces. In the present study, we propose a hybrid approach for weapon systems selection that combines analytic hierarchy process (AHP) and principal component analysis (PCA) to determine the weights to assign to the factors that go into these selection decisions. These weights are inputted into a goal programming (GP) model to determine the best alternative among the weapon systems. The proposed …

Feature selection based on an ensemble classifier has been recognized as a crucial technique for modeling high-dimensional data. Feature selection based on the random forests model, which is constructed by aggregating multiple decision tree classifiers, has been widely used. However, a lack of stability and balance in decision trees decreases the robustness of random forests. This limitation motivated us to propose a feature selection method based on newly designed nearest-neighbor ensemble classifiers. The proposed method finds significant features by using an iterative procedure. We performed experiments with 20 datasets of microarray gene expressions to examine the property of the proposed method and compared it with random forests. The results demonstrated the effectiveness and robustness of the proposed method, especially when the number of features exceeds the number of observations.

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems. Recommender systems are personalized information filtering used to identify a set of items that will be of interest to a certain user. This paper reviews recommender systems and presents their pros and cons.

This study presents three feature selection methods for identifying the metabolite features in nuclear magnetic resonance spectra that contribute to the distinction of samples among varying nutritional conditions. Principal component analysis, Fisher discriminant analysis, and Partial Least Square Discriminant Analysis (PLS-DA) were used to calculate the importance of individual metabolite feature in spectra. Moreover, an Orthogonal Signal Correction (OSC) filter was used to eliminate unnecessary variations in spectra. We evaluated the presented methods by comparing the ability of classification based on the features selected by each method. The result showed that the best classification was achieved from an OSC-PLS-DA model.

We present a hybrid algorithm called parallel simulated annealing with a greedy algorithm (PSAGA) to learn Bayesian network structures. This work focuses on simulated annealing and its parallelization with memoization to accelerate the search process. At each step of the local search, a hybrid search method combining simulated annealing with a greedy algorithm was adopted. The proposed PSAGA aims to achieve both the efficiency of parallel search and the effectiveness of a more exhaustive search. The Bayesian Dirichlet equivalence metric was used to determine an optimal structure for PSAGA. The proposed PSAGA was evaluated on seven well-known Bayesian network benchmarks generated at random. We first conducted experiments to evaluate the computational time performance of the proposed parallel search. We then compared PSAGA with existing variants of simulated annealing-based …

Multivariate control charts have been widely used in many industries to monitor and diagnose processes characterized by a large number of quality characteristics. Usually, these characteristics are highly correlated with each other. The direct use of conventional multivariate control charts for situations with highly correlated characteristics may lead to increased rates of false alarms. Principal component analysis (PCA) control charts have been widely used to address problems posed by such high correlations by transforming the set of correlated variables to an uncorrelated set of variables and then identifying the PCs with highest contribution which then allows one to reduce dimensionality. However, an assumption that the data are normally distributed underlies the construction of the control limits of traditional PCA control charts. This assumption has limited the use of PCA control charts in nonnormal situations …

This research develops a novel data-integrated sim ulation to evaluate n urse–patient a ssignments (SIMNA) based on a real data set provided by a northeast Texas hospital. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for the simulation. Classification and Regression Tree models, data mining tools for prediction and classification, were used to develop five tree structures: (a) four classification trees from which transition probabilities for nurse movements are determined, and (b) a regression tree from which the amount of time a nurse spends in a location is predicted based on factors such as the primary diagnosis of a patient and the type of nurse. Kernel density estimation is used to estimate the continuous distribution for the amount of time a nurse spends in a location. Results obtained from SIMNA to evaluate nurse–patient assignments in …

Various control chart methods have been used in healthcare and public health surveillance to detect increases in the rates of diseases or their symptoms. Although the observations in many health surveillance applications are often discrete, few efforts have been made to ex-plore the behavior of detection methods in discrete distributions. Joner et al.(Statist. Med. 2008; 27: 2555–2575) investigated and compared the performance of the scan statistic methods with the cumulative sum (CUSUM) charts under a Bernoulli distribution. In this paper we compare the performance of three detection methods: temporal scan statistic, CUSUM, and exponential weighted moving average (EWMA) when the observations follow the Poisson distribution. A simulation study showed that the Poisson CUSUM and EWMA charts generally outperformed the Poisson scan statistic methods. In comparisons between CUSUM and EWMA, the …

Identification of meaningful patterns and trends in large volumes of text data is an important task in various research areas. In the present study we crawled the keywords from the abstracts in IIE Transactions, one of the representative journals in the field of Industrial Engineering from 1969 to 2011. We applied low-dimensional embedding method, clustering analysis, association rule, and social network analysis to find meaningful associative patterns of key words frequently appeared in the paper.

Extracting useful and meaningful patterns from large volumes of text data is of growing importance. In the present study we analyze vast amounts of prescription data, generated from the book of oriental medicine to identify the relationships between the symptoms and the associated medicines used to treat these symptoms. The oriental medicine book used in this study (called Bangyakhappyeon) contains a large number of prescriptions to treat about 54 categorized symptoms and lists the corresponding herbal materials. We used an association rule algorithm combined with network analysis and found useful and informative relationships between the symptoms and medicines.

Processes characterized by high dimensional and mixture data challenge traditional statistical process control charts. In this study, we propose a multivariate control chart based on the Gower distance that can handle a mixture of continuous and categorical data. An extensive simulation study was conducted to examine the properties of the proposed control chart under various scenarios and compared it with some existing multivariate control charts. The simulation results revealed that the proposed control chart outperformed the existing charts when the number of categorical variables increases. Furthermore, we demonstrated the applicability and effectiveness of the proposed control charts through a real case study.

Data Mining & Quality Analytics Lab

교수님 소개

김성범

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Policy likelihood-based query sampling and critic-exploited reset for efficient preference-based reinforcement learning

CATS-RAG: Contextual augmented triplet synthesis for RAG in technical QA

Semi-supervised learning under extreme class imbalance in wafer bin map defect pattern classification

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

김성범

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Policy likelihood-based query sampling and critic-exploited reset for efficient preference-based reinforcement learning

CATS-RAG: Contextual augmented triplet synthesis for RAG in technical QA

Semi-supervised learning under extreme class imbalance in wafer bin map defect pattern classification

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집