http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Kim, Hyung-Mo,Kang, Yoo-Lee,Chung, Woo-Jae,Kyeong, San,Jeong, Sinyoung,Kang, Homan,Jeong, Cheolhwan,Rho, Won-Yeop,Kim, Dong-Hyuk,Jeong, Dae Hong,Lee, Yoon-Sik,Jun, Bong-Hyun Elsevier 2015 Journal of industrial and engineering chemistry Vol.21 No.-
<P><B>Abstract</B></P> <P>A better understanding of protein–protein interactions can be obtained from multiplex protein detection technologies, and spectrally encoded beads can provide fast and efficient means for this type of detection methods. However, high-throughput detection is challenging due to the requirement of using labeled secondary proteins to detect protein binding events. We have previously reported that polydiacetylene-coated surface-enhanced Raman scattering-encoded beads (PDA–SERS beads) can provide an enhanced encoding capacity owing to their SERS properties as well as their potential for label-free detection from the PDA layer. In this study, we introduced ligands to the PDA–SERS beads by using methods for making free-floating vesicles and planar solid substrates, which enabled the detection of target proteins by PDA fluorescence in a PDA–SERS beads system. By using PDA–SERS beads immobilized with biotin, the fluorescence intensities of biotin-conjugated PDA–SERS beads were increased with an increase in the concentration of streptavidin. And, we could detect 2×10<SUP>−8</SUP> M of streptavidin by measuring the fluorescence intensity without the requirement of an additional labeling step.</P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Kim, Cheolhwan,Shin, Dongyeob,Kim, Bohun,Park, Jongsun IEEE 2018 IEEE journal on emerging and selected topics in ci Vol.8 No.4
<P>In convolutional neural networks (CNNs), convolutional layers consume dominant portion of computation energy due to large amount of multiply-accumulate operations (MACs). However, those MACs become meaningless (zeroes) after rectified linear unit when the convolution results become negative. In this paper, we present an efficient approach to predict and skip the convolutions generating zero outputs. The proposed two-step zero prediction approach, called mosaic CNN, can be effectively used for trading off classification accuracy for computation energy in CNN. In the mosaic CNN, the outputs of each convolutional layer are computed considering their spatial surroundings in an output feature map. Here, the types of spatial surroundings (mosaic types) can be selected to save computation energy at the expense of accuracy. In order to further save the computations, we also propose a most significant bits (MSBs) only computation scheme, where a constant value representing least significant bits compensates the MSBs only computations. The CNN accelerator supporting the combined two approaches has been implemented using the 65-nm CMOS process. The numerical results show that compared with the state-of-art processor, the proposed reconfigurable accelerator can achieve energy savings ranging from 16.99% to 29.64% for VGG-16 without seriously compromising the classification accuracy.</P>
DSP 시스템을 위한 데이터보존력 인지 가변구조형 eDRAM기반의 LIFO 메모리 설계
김철환(Cheolhwan Kim),박병길(Byeonggil Park),강규성(Gyuseong Kang),박종선(Jongsun Park) 대한전자공학회 2016 대한전자공학회 학술대회 Vol.2016 No.6
This paper proposes new design strategies to deal with retention changes of eDRAM-based LIFO memory for DSP system. The proposed design ensures the reliability of data when retention is reduced by half due to temperature change. Simulation results from a Viterbi decoder implemented in a 65nm CMOS technology demonstrate the effectiveness of the proposed design strategies.
MMS 포인트 클라우드를 활용한 하천제방 경사도 자동 추출에 관한 연구
김철환 ( Cheolhwan Kim ),이지상 ( Jisang Lee ),최원준 ( Wonjun Choi ),김원대 ( Wondae Kim ),손홍규 ( Hong-gyoo Sohn ) 대한원격탐사학회 2021 大韓遠隔探査學會誌 Vol.37 No.5
하천 시설물의 효율적인 유지관리를 위해서는 대상물에 대해 지속적이고, 주기적인 데이터 취득이 선행되어야 한다. 하천 시설물은 일반 시설물과 달리 넓고 긴 지역을 따라 분포하고 있으므로 지상레이저스캐너, 토탈스테이션 및 GNSS를 활용하는 기존의 하천 측량 방법으로는 공간정보를 취득하는 데에 비용·인력·시간적 한계가 존재한다. 이에 반해, 모바일매핑시스템(Mobile Mapping System, 이하 MMS)은 플랫폼의 이동과 동시에 3차원 공간정보를 취득하므로 하천 시설물의 데이터 취득에 효율적이다. 따라서 본 연구진은 MMS를 활용하여 안양천 4 km 제방에 대해 20분동안 184,646,099개의 포인트를 취득했으며, 이를 10 m 간격의 종 방향으로 분할하여 378개의 횡단면을 추출하였다. 제방 횡단면 포인트 클라우드에서 제외지의 경사면 정보만 따로 분리하여 최대 및 평균 비탈 경사를 자동으로 계산하였으며, 이를 동일 제방에 대해 수동으로 계산한 값과 비교했을 때 RMSE 기준 최대 경사 1.124°, 평균 경사 1.659°의 정확도를 확인할 수 있었다. Reference 경사는 제방의 포인트 클라우드를 plot하고 경사 계산 시 위치정보를 사용하는 두 점을 직접 선택하여 수동으로 계산하였다. 또한 자동 추출한 경사를 하천기본계획 상의 비탈 경사면 설계 기준과 비교하여 MMS를 활용한 하천 시설물 검사의 가능성을 확인하였다. Continuous and periodic data acquisition must be preceded to maintain and manage the river facilities effectively. Adapting the existing general facilities methods, which include river surveying methods such as terrestrial laser scanners, total stations, and Global Navigation Satellite System (GNSS), has limitation in terms of its costs, manpower, and times to acquire spatial information since the river facilities are distributed across the wide and long area. On the other hand, the Mobile Mapping System (MMS) has comparative advantage in acquiring the data of river facilities since it constructs three-dimensional spatial information while moving. By using the MMS, 184,646,009 points could be attained for Anyang stream with a length of 4 kilometers only in 20 minutes. Levee points were divided at intervals of 10 meters so that about 378 levee cross sections were generated. In addition, the water side maximum and average slope could be automatically calculated by separating slope plane form levee point cloud, and the accuracy of RMSE was confirmed by comparing with manually calculated slope. The reference slope was calculated manually by plotting point cloud of levee slope plane and selecting two points that use location information when calculating the slope. Also, as a result of comparing the water side slope with slope standard in basic river plan for Anyang stream, it is confirmed that inspecting the river facilities with the MMS point cloud is highly recommended than the existing river survey.