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장민규(Mingyu Jang),Swati Singh(Swati Singh ),서준기(Joonki Suh) 한국세라믹학회 2023 세라미스트 Vol.26 No.1
Understanding thermal energy transport of crystalline materials, often highly dependent on their crystalline directions, is crucial for energy harvesting and thermal management applications. In this sense, anisotropy in thermal conductivity (κ), which is the unique characteristic of two-dimensional (2D) materials involving graphene and transition metal dichalcogenides (TMDs), has been attracting tremendous attention in terms of fundamental science and application-driven technology aspects. This distinctive heat transport behavior of 2D van der Waals (vdW) materials generally originates from their intrinsic crystal structures and associated lattice vibrations. Here, we thoroughly review and summarize the anisotropic thermal conductivity in 2D vdW crystals in two different categories: 1) in-plane vs. out-of-plane and 2) between two different in-plane directions. In addition, we introduce a range of thermal conductivity measurement techniques that can be employed for 2D vdW materials provided with their working principles, advantages, and limitations. Beyond their intrinsic anisotropic ratio, we conclude with perspectives on the extrinsic modulations of thermal conductivities, thereby maximizing it toward effective thermal management.
차량 주행 시뮬레이터 기반의 Sensor Fusion Algorithm 검증 환경 구축 및 평가
장민규(MinGyu Jang),구태윤(TaeYun Koo),한종철(JongChul Han),이한주(HanJu Lee),문진동(JinDong Moon) 한국자동차공학회 2012 한국자동차공학회 부문종합 학술대회 Vol.2012 No.5
ADAS(Advanced Driver Assist System) are systems to help the driver and each system has specific software made by developer. Unfortunately, a developer can make an error or mistake, which causes a failure in system. So software testing is very important process and needed for drivers safety. In this study, we focus on sensor fusion system(LKAS and SCC) and propose the software test environment of sensor fusion algorithm using a driving simulator. The HILS(Hardware-in-the-Loop Simulation) test environment has been established and evaluated its performance.
이동은(Dongeun Lee),장민규(Mingyu Jang),윤동원(Dongweon Yoon) 한국정보기술학회 2021 Proceedings of KIIT Conference Vol.2021 No.11
최신 협력 및 비협력 통신 환경에서 가장 중요한 기술 중 하나는 블라인드 자동 변조 분류 (automatic modulation classification, AMC)로 최근 머신 러닝을 AMC에 접목한 연구들이 많이 진행되고 있다. 특히 AMC는 단일 반송파 전송 방식 보다 직교 주파수 분할 다중 방식(orthogonal frequency division multiplexing, OFDM) 에서 더 도전적인 과제이다. 본 논문에서는 딥 러닝 알고리즘 중 합성곱 신경망(convolutional neural network) 을 기반으로 OFDM 신호의 변조 방식을 식별하는 방법을 제안하고 컴퓨터 모의실험을 통해 분류 성능을 분석하여 유효성을 검증한다. In contemporary cooperative and non-cooperative communication contexts, blind automatic modulation classification (AMC) has become one of the most important techniques. Recently, many studies on AMC using machine learning have been conducted in many places in the literature. In particular, AMC in orthogonal frequency division multiplexing (OFDM) system becomes a more challenging task than that in a single carrier system. In this paper, we propose an improved AMC based on convolutional neural network to classify modulation type of OFDM signal and validate the proposed AMC through computer simulations.