https://scholar.dgist.ac.kr/handle/20.500.11750/95 2026-04-05T14:27:45Z https://scholar.dgist.ac.kr/handle/20.500.11750/58976 Title: A Memetic Algorithm for Cache-aided Data Broadcast with Network Coding in Vehicular Networks Author(s): Liu, Kai; Feng, Liang; Dai, Penglin; Wu, Weiwei; Lee, Victor C. S.; Son, Sang Hyuk Abstract: With recent advances in wireless communications, vehicular networks are envisioned as a promising paradigm on achieving breakthroughs in transportation safety, efficiency, and sustainability. This work investigates data broadcast via Infrastructure-to-Vehicle (I2V) communication by exploiting the vehicular caching and network coding for enhancing bandwidth efficiency of the road-side unit (RSU). Specifically, we present an architecture for providing real-time data services via I2V communication in the service range of a RSU. Then, we investigate the problem of cache-aided data dissemination with network coding and prove that it is NP-hard. Further, we propose a memetic algorithm, which consists of a binary vector representation for encoding solutions, a fitness function for solution evaluation, a set of operators for offspring generation, a local search method for solution enhancement and a repair operator for fixing infeasible solutions. Finally, we build the simulation model and give a comprehensive performance evaluation to demonstrate the superiority of the proposed solution. © 2017 IEEE. 2017-12-04T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/58927 Title: A Multi-Agent DRL-Based Method for Cooperatively Determining Coordination and Lane-Change of Vehicles at Signal-Free Intersections With Free-Direction Lanes Author(s): Nie, Wendi; Gao, Deya; Liu, Chaofan; Duan, Yaoxin; Lee, Victor C. S.; Liu, Kai; Jason Xue, Chun; Gui, Guan; Hyuk Son, Sang Abstract: Owing to the growing population and rapid urbanization, intersections, where traffic converges from various directions, have become major bottlenecks for road capacity due to frequent congestion. Recent advances in Connected and Autonomous Vehicle (CAV) technology enable signal-free intersections, where CAVs collaborate to cross intersections without collisions. Most existing signal-free intersection control methods focus on accommodating conflicts among vehicles inside the intersection and fixed-direction lanes are commonly adopted. However, the use of fixed-direction lanes is a legacy from conventional signalized intersections, where turning lanes are predetermined and fixed, so as to direct vehicles with different turning intentions to different lanes and avoid collisions. In this paper, we aim to make full utilization of the capacity of signal-free intersections by making use of free-direction lanes, which allow vehicles to make right, straight or left turns from any lane. To this end, we propose a cooperative multi-agent Deep Reinforcement Learning (DRL)-based control method for signal-free intersections with free-direction lanes. Specifically, we first study the problem of cooperatively determining coordination of vehicles inside the intersection and lane changes of vehicles on the incoming arms. Then, a multi-agent DRL-based control method for cooperatively determining coordination and lane-change of vehicles for signal-free intersections with free-direction lanes, named CD-CLC, is proposed for maximizing non-conflicting vehicles crossing the intersection simultaneously while taking vehicle fairness into consideration, to minimize travel delays of vehicles and improve traffic efficiency. Extensive experiments have been conducted to compare CD-CLC with other state-of-the-art methods to demonstrate the effectiveness of the proposed approach. © 2014 IEEE. 2025-08-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/47338 Title: When thermal control meets sensor noise: Analysis of noise-induced temperature error Author(s): Kim, Dohwan; Park, Kyung-Joon; Eun, Yongsoon; Son, Sang Hyuk; Lu, Chenyang Abstract: Thermal control is critical for real-time systems as overheated processors can result in serious performance degradation or even system breakdown due to hardware throttling. The major challenges in thermal control for real-time systems are (i) the need to enforce both real-time and thermal constraints; (ii) uncertain system dynamics; and (iii) thermal sensor noise. Previous studies have resolved the first two, but the practical issue of sensor noise has not been properly addressed yet. In this paper, we introduce a novel thermal control algorithm that can appropriately handle thermal sensor noise. Our key observation is that even a small zero-mean sensor noise can induce a significant steady-state error between the target and the actual temperature of a processor. This steady-state error is contrary to our intuition that zero-mean sensor noise induces zero-mean fluctuations. We show that an intuitive attempt to resolve this unusual situation is not effective at all. By a rigorous approach, we analyze the underlying mechanism and quantify the noised-induced error in a closed form in terms of noise statistics and system parameters. Based on our analysis, we propose a simple and effective solution for eliminating the error and maintaining the desired processor temperature. Through extensive simulations, we show the advantages of our proposed algorithm, referred to as Thermal Control under Utilization Bound with Virtual Saturation (TCUB-VS). © 2015 IEEE. 2015-04-13T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/47272 Title: 학습 데이터의 도메인 적응(domain adaptation)에 효과적인 데이터 보충(data augmentation) 기법 연구 Author(s): 박지철; 최민국; 이진희; 권순; 손상혁 Abstract: 본 연구는 효과적인 객체 검출을 위한 CNN (Convolutional Neural Network) 아키텍처의 도메인 적응(domain adaptation) 시 나타날 수 있는 문제점을 해결한다. 일반적으로 특정 과업(task)에 유용한 CNN 아키텍처를 설계할 경우 학습과 성능 검증을 위해 공개된 데이터셋(open dataset)을 활용한다. 공개된 데이터셋은 주어진 과업에 따라 다수의 부류(class)를 포함하는 일반화된(generalized) 데이터 수집을 목표로 하고 있다. 이로 인해 공개된 학습 데이터는 특정 과업에 특화된 실 사례 데이터와 서로 다른 확률분포를 갖게 되고, 이로 인해 공개된 데이터셋과 실 사례에 적용 결과에 큰 성능 차이를 보이게 되고 이를 위해 다양한 도메인 적응 기법을 적용하게 된다. 본 논문에서는 객체 검출을 위한 CNN 아키텍처의 도메인 적응 문제를 해결하기 위해, 알려진 데이터 셋에서 학습된 최신 기술의 CNN 아키텍처의 학습 결과를 보완하는 데이터 보충 (data augmentation) 기법을 제안한다. 이를 해결하기 위해 실 사례 객체 검출 학습 데이터 보충을 위한 어노테이션 도구(annotation tool)을 제작하였으며, 제안된 데이터 보충 기법으로 실 사례 검출 결과가 크게 향상 되는 것을 확인할 수 있다. 2017-02-16T15:00:00Z