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Electrodeposition에 의해 성장온도와 시간을 달리하여 성장한 ZnO 나노구조의 특성
박영빈(Youngbin Park),남기웅(Giwoong Nam),박선희(Seonhee Park),문지윤(Jiyun Moon),김동완(Dongwan Kim),강해리(Hae Ri Kang),김하은(Haeun Kim),이욱빈(Wookbin Lee),임재영(Jae-Young Leem) 한국표면공학회 2014 한국표면공학회지 Vol.47 No.4
The electrodeposition of ZnO nanorods was performed on ITO glass. The optimization of two process parameters (solution temperature and growth time) has been studied in order to control the orientation, morphology, density, and growth rate of ZnO nanorods. The structural and optical properties of ZnO nanorods were systematically investigated by using field-emission scanning electron microscopy, X-ray diffractometer, and photoluminescence. Commonly, the results of the structural property show that hexagonal ZnO nanorods with wurtzite crystal structures have a c-axis orientation, and higher intensity for the ZnO (002) diffraction peaks. Furthermore, the nanorods length increased with increasing both the solution temperature and the growth time. The results of the optical property show a strong UV (3.28 eV) peaks and a weak visible (1.9~2.4 eV) bands, the intensity of UV peaks was increased with increasing both the solution temperature and the growth time. Especially, the UV peak for growth of nanorods at 75oC blue-shift than different temperatures.
전착법으로 성장된 산화아연 나노막대의 특성에 전구체 농도 및 전착 전류가 미치는 효과
박영빈(Youngbin Park),남기웅(Giwoong Nam),박선희(Seonhee Park),문지윤(Jiyun Moon),김동완(Dongwan Kim),강해리(Hae Ri Kang),김하은(Haeun Kim),이욱빈(Wookbin Lee),임재영(Jae-Young Leem) 한국표면공학회 2014 한국표면공학회지 Vol.47 No.4
ZnO nanorods have been deposited on ITO glass by electrodeposition method. The optimization of two process parameters (precursor concentration and current) has been studied in order to control the orientation, morphology, and optical property of the ZnO nanorods. The structural and optical properties of ZnO nanorods were systematically investigated by using field-emission scanning electron microscopy, X-ray diffractometer, and photoluminescence. Commonly, the results show that ZnO nanorods with a hexagonal form and wurtzite crystal structure have a c-axis orientation and higher intensity for the ZnO (002) diffraction peaks. Both high precursor concentration and high electrodeposition current cause the increase in nanorods diameter and coverage ratio. ZnO nanorods show a strong UV (3.28 eV) and a weak visible (1.9 ~ 2.4 eV) bands.
퍼콜레이션 문턱값 예측을 위한 그래핀 고분자 나노복합재의 모델링
김명수(Myungsoo Kim),박영빈(Youngbin Park),정호순(Hosoon Jung) 한국자동차공학회 2011 한국자동차공학회 지부 학술대회 논문집 Vol.2011 No.10-2
Extraordinary physical properties of graphene sheets enable them to be an excellent reinforcement in polymer matrix. This research explores modeling of graphene polymer nanocomposites for prediction of electrical percolation threshold. Graphene sheets were assumed to be square in the simulation. To obtain percolation probability, a percolation model including site and bond and Monte Carlo technique were employed. The results of the simulation showed transition, from nonconducting material to conducting material, and increase of electrical conductivity with the increase of graphene loading in a polymer.
원종순(Jongsoon Won),박영빈(Youngbin Park),서일홍(Il Hong Suh) 대한전자공학회 2018 대한전자공학회 학술대회 Vol.2018 No.6
In this study, we aim at grasping a single target object in a clutter environment using a robotic arm. For this purpose, we add a primitive motion termed “scattering” to a set of manipulator capabilities. In particular, a robot aims to grasp an object by first executing a simple scattering motion to scatter objects near the target object that might be blocking direcr access to it. The robot then reaches out its arm and finally closes its fingers to grasp the target object. Through experiments, we investigate the improvements in success rate with robotic grasping with the inclusion of scattering based on a relatively simple machine learning algorithm as well as well-known visual segmentation algorithms.
클러터 환경에서 로봇 파지를 위한 역학 예측 신경망 기반 강화학습 방법
김병완(Byung Wan Kim),박영빈(Youngbin Park),서일홍(Il Hong Suh) 대한전자공학회 2021 전자공학회논문지 Vol.58 No.11
본 연구는 둘 이상의 물체가 서로 겹치거나 인접하여 로봇 파지 포인트를 파악하기 어려운 클러터 환경(cluttered environment)에서 역학 예측 신경망(Dynamics Prediction Network, DPN)을 이용하여 작업환경과 로봇 행동 사이의 역학 관계를 학습하고 파지 작업을 수행하는 강화학습 방법을 제안한다. 클러터 환경에서 파지 포인트를 찾을 수 없는 상황을 해결하기 위해 비파지 동작을 활용하며, 제안하는 역학 예측 신경망을 이용한 강화학습 방법은 파지뿐 아니라 비파지 동작 학습에서도 효과적임을 보여준다. 학습 환경을 추계(stochastic world)가 아닌 결정계(deterministic world)로 가정하여 전이 확률(state transition probability)을 1로 고정함으로써 DPN은 현재 상태(current state), 현재 행동(current action), 다음 상태(next state)의 일대일 대응관계를 학습한다. 학습 단계에서 추가적으로 진행되는 DPN으로 인한 학습 부담을 줄이기 위하여 매개변수 공유(parameter sharing)를 학습 모델에 적용하였다. 제안하는 방법은 매개변수 공유와 DPN으로 학습에서의 부담뿐 아니라 동작모델의 가중치 메모리 크기를 절반으로 줄이면서도 블록 테스트 환경에서 8%의 파지 성공률 향상을 보였다. 실세계 물체를 3D 스캔하여 구성한 새로운 테스트 환경 실험에서 23%의 파지 성공률 향상을 보여 물리적 상호작용 학습으로 일반화(generalization)가 잘 이루어졌음을 보였다. In this paper, we propose a reinforcement learning method to learn the dynamic relationship between the workspace and robot actions using Dynamics Prediction Network(DPN). We perform the grasping tasks with this deep Q network model in cluttered environments where it is difficult to find the grasp point because two or more objects overlap or adjoin each other. We use non-prehensile actions to solve the situation in which the grasp point cannot be found in the cluttered environment. DPN learning is effective in learning not only prehensile actions but also non-prehensile actions. Assuming that the learning environment is a deterministic world rather than a stochastic world, then the state transition probability is fixed at 1. The DPN learns one-to-one correspondence between the current state, current actions, and next state. Parameter sharing was applied to the training model in order to reduce the training computations due to the DPN model being added during the training phase. The proposed method applied both parameter sharing and DPN appropriately, reducing the burden on learning as well as the size of the weight memory of the operating model by half, and showed an 8% improvement in the grasping success rate of the block test environments. In the experiments of new test environments constructed by 3D scanning objects of the real world, the grasping success rate improved by 23%, showing that the generalization of physical interaction learning was well done.