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Jihyung Lee

Sungkyunkwan University · Computer Science

About the Lab

Professor Jihyung Lee's research lab specializes in intelligent systems and data-driven optimization, focusing on real-world applications of machine learning, particularly in traffic control, energy management, and industrial scheduling. The lab develops advanced algorithms such as fuzzy logic controllers, federated learning with privacy preservation, and reinforcement learning for complex decision-making in dynamic environments. A key emphasis is placed on integrating domain expertise and practical constraints to ensure that clustering and optimization results are not only statistically sound but also applicable in real-world systems like smart cities and smart factories. The lab combines theoretical innovation with simulation-based performance evaluation to address challenges in scalability, data privacy, and system adaptability.

federated learningtraffic controlreinforcement learningcustomer clusteringfeature selection

Research Overview

Papers
323
Total Citations
2,034
Papers (5y)
49
Primary Field
Computer Science

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
49total
2022
2023
2024
2025
2026
Citations per year (5y)
144total
20222023202420252026

Selected Papers

15
1
Article|129 citations·1999
Distributed and cooperative fuzzy controllers for traffic intersections group
Jee-Hyong Lee, Hyung Lee-Kwang
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)

This paper presents a fuzzy traffic controller for a set of intersections and its simulation results. The controller of an intersection controls its own traffic and cooperates with its neighbors. It gets information from its traffic detectors and its neighbors. Using this information, the fuzzy rule base system gives optimal signals. It manages phase sequences and phase lengths adaptively to its neighbors' as well as its own traffic conditions. To carry out the performance evaluation of the cont

Control and Systems EngineeringEngineering
2
Article|25 citations·2001
Comparison of fuzzy values on a continuous domain
Jee-Hyong Lee, Hyung Lee-Kwang
SJR Q1Fuzzy Sets and Systems
Management Science and Operations ResearchDecision Sciences
3
Article|23 citations·2014
Noisy and incomplete fingerprint classification using local ridge distribution models
Hye-Wuk Jung, Jee-Hyong Lee
SJR Q1Pattern Recognition
Signal ProcessingComputer Science
4
Article|23 citations·2022
Weighted Averaging Federated Learning Based on Example Forgetting Events in Label Imbalanced Non-IID
Mannsoo Hong, Seok–Kyu Kang, Jee-Hyong Lee
SJR Q2Applied SciencesOA

Federated learning, a data privacy-focused distributed learning method, trains a model by aggregating local knowledge from clients. Each client collects and utilizes its own local dataset to train a local model. Local models in the connected federated learning network are uploaded to the server. In the server, local models are aggregated into a global model. During the process, no local data is transmitted in or out of any client. This procedure may protect data privacy; however, federated learn

Artificial IntelligenceComputer Science
5
Article|18 citations·2015
Electricity Customer Clustering Following Experts’ Principle for Demand Response Applications
Jimyung Kang, Jee-Hyong Lee
SJR Q1EnergiesOA

The clustering of electricity customers might have an effective meaning if, and only if, it is verified by domain experts. Most of the previous studies on customer clustering, however, do not consider real applications, but only the structure of clusters. Therefore, there is no guarantee that the clustering results are applicable to real domains. In other words, the results might not coincide with those of domain experts. In this paper, we focus on formulating clusters that are applicable to rea

Electrical and Electronic EngineeringEngineering
6
Article|17 citations·2018
MOTiFS: Monte Carlo Tree Search Based Feature Selection
Muhammad Umar Chaudhry, Jee-Hyong Lee
SJR Q2EntropyOA

Given the increasing size and complexity of datasets needed to train machine learning algorithms, it is necessary to reduce the number of features required to achieve high classification accuracy. This paper presents a novel and efficient approach based on the Monte Carlo Tree Search (MCTS) to find the optimal feature subset through the feature space. The algorithm searches for the best feature subset by combining the benefits of tree search with random sampling. Starting from an empty node, the

Artificial IntelligenceComputer Science
7
Article|16 citations·2002
Fuzzy controller for intersection group
Jee-Hyong Lee, Keon Myung Lee, Hyung Lee-Kwang

This paper presents a fuzzy traffic controller for a set of intersections and its simulation results. In the developed system, each intersection has its own traffic controller. The controller of a intersection manages the phase sequence and the phase length dynamically according to its own and the neighboring traffic situations. To do this, we adopt a competitive scheme. All possible phases except the green phase compete to get the green signal. The controller consists of three modules: the next

Control and Systems EngineeringEngineering
8
Article|15 citations·2022
Deep Reinforcement Learning Approach for Material Scheduling Considering High-Dimensional Environment of Hybrid Flow-Shop Problem
Chang-Bae Gil, Jee-Hyong Lee
SJR Q2Applied SciencesOA

Manufacturing sites encounter various scheduling problems, which must be dealt with to efficiently manufacture products and reduce costs. With the development of smart factory technology, many elements at manufacturing sites have become unmanned and more complex. Moreover, owing to the mixing of several processes in one production line, the need for efficient scheduling of materials has emerged. The aim of this study is to solve the material scheduling problem of many machines in a hybrid flow-s

Industrial and Manufacturing EngineeringEngineering
9
Article|11 citations·2007
Artificial Intelligence in Computer Games
Jee-Hyong Lee, Tae Bok Yoon
한국지능시스템학회 국제학술대회 발표논문집
Artificial IntelligenceComputer Science
10
Article|11 citations·2013
An approach of genetic programming for music emotion classification
Sung-Woo Bang, Jaekwang Kim, Jee-Hyong Lee
SJR Q2International Journal of Control Automation and Systems
Signal ProcessingComputer Science
11
Article|10 citations·2018
A Television Recommender System Learning a User’s Time-Aware Watching Patterns Using Quadratic Programming
Noo-ri Kim, Sungtak Oh, Jee-Hyong Lee
SJR Q2Applied SciencesOA

In this paper, a novel television (TV) program recommendation method is proposed by merging multiple preferences. We use channels and genres of programs, which is available information in standalone TVs, as features for the recommendation. The proposed method performs multi-time contextual profiling and constructs multiple-time contextual preference matrices of channels and genres. Since multiple preference models are constructed with different time contexts, there can be conflicts among them. I

Information SystemsComputer Science
12
Article|10 citations·2017
Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers
Jimyung Kang, Jee-Hyong Lee
SJR Q1EnergiesOA

Demand response is nowadays considered as another type of generator, beyond just a simple peak reduction mechanism. A demand response service provider (DRSP) can, through its subcontracts with many energy customers, virtually generate electricity with actual load reduction. However, in this type of virtual generator, the amount of load reduction includes inevitable uncertainty, because it consists of a very large number of independent energy customers. While they may reduce energy today, they mi

Electrical and Electronic EngineeringEngineering
13
Article|9 citations·2002
A method for ranking fuzzily fuzzy numbers
Jee-Hyong Lee, Hyung Lee-Kwang

Ranking fuzzy numbers is one of the very important research topics in fuzzy set theory because it is a base of decision-making in all application areas. However, fuzzy numbers cannot be easily arranged in order of magnitude because they represent uncertain and vague values. When two fuzzy numbers overlap with each other, a fuzzy number may not be considered absolutely larger than the other. That is, even though a fuzzy number may be considered larger than the other, it may also be considered sma

Management Science and Operations ResearchDecision Sciences
14
Article|9 citations·2007
TRAFFIC CONTROL OF INTERSECTION GROUP BASED ON FUZZY LOGIC
Jee-Hyong Lee, Keon Myung Lee, Kyoung-A Seong, ChangBum Kim, Hyung Lee-Kwang

. This paper presents a traffic fuzzy controller for a set of intersections and its simulation results. To control a set of intersection we distribute controls to the controller at each intersection. The controller at an intersection manages phase sequences and phase lengths adaptively to its neighborhood's as well as its own traffic conditions. The simulation shows good performance in the case of time-varying traffic patterns and heavy traffic conditions. 1. Introduction There are many

Artificial IntelligenceComputer Science
15
Article|8 citations·2018
A novel recommendation approach based on chronological cohesive units in content consuming logs
Jaekwang Kim, Jee-Hyong Lee
SJR Q1Information Sciences
Computer Vision and Pattern RecognitionComputer Science

Research Areas

Artificial IntelligenceInformation SystemsSignal ProcessingComputer Networks and CommunicationsIndustrial and Manufacturing EngineeringComputer Vision and Pattern Recognition

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