http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Locality Condition on the cwu- ~ talAllomorphy of GIVE in Korean
Minjung Kim,Inkie Chung 한국생성문법학회 2017 생성문법연구 Vol.27 No.3
Kim, Minjung, Chung, Inkie. 2017. Locality Condition on the cwu- ~ talAllomorphy of GIVE in Korean. Studies in Generative Grammar, 27-3, 611-630. This study argues that the verb form tal- is one of the suppletive allomorphs of the verb GIVE in Korean and presents a Distributed Morphology account for the suppletion of cwu- ~ tal- for GIVE and the locality mechanism related to this morphosyntactic alternation. First, this study provides the precise morphosyntactic environment of tal-. Second, it presents the situations where this expected suppletion is blocked by an intervening element between the root and the conditioning element(s). Then, it presents a Distributed Morphology analysis of the cwu- ~ talsuppletion. Based on these observations and analysis, we argue that the apparent long-distance conditioning of this tal- suppletion is actually limited within the domain of word as suggested by Bobaljik (2012).
The Analysis of New Generation S-DMB system
InKi Lee,DaeIg Chang 대한전자공학회 2007 ITC-CSCC :International Technical Conference on Ci Vol.2007 No.7
Current S-DMB service in the Korea, one of such personal mobile multimedia services, provides broadcasting with 11 video and 26 audio channels. To provide this service more differently from other multimedia services, enhancement in transmission quality or increase in transmission bandwidth is requested. But this enhancement(or increase) should be Backwards Compatible(BC) to the legacy system for saving system migration expenses. In this paper, we propose a method for enhancing transmission quality or bandwidth, using H- 16APSK for BC and LDPC for powerful channel code. As the enhancement in transmission rate can be allowed as far as the resulted performance degradation and the resulted increase in transmission rate have been simulated and analyzed.
Dynamic measurement of stress optical behavior of three amorphous polymers
Inki Min,Kyunghwan Yoon 한국유변학회 2012 Korea-Australia rheology journal Vol.24 No.1
In the present study, rheo-optical and mechanical properties of three amorphous polymers, i.e., PS (polystyrene), PC(polycarbonate) and COC(cyclo olefin copolymer), widely used for optical products have been investigated. Accurate measurement of stress optical coefficients and elastic modulus data across the glass transition region are essential for predicting optical anisotropy in many injection molded optical products like pickup lenses and waveguide in LCD module since the final products have both flow and thermal history from the melt to glass. To obtain stress optical behavior in wide range of frequency and temperature including rubbery, glassy and glass transition regime, frequency sweep tests with extensional bar and shear sandwich tools were undertaken. As a result, glassy and melt extreme values of stress optical coefficient of PS and PC were evaluated as well as master plots in wide frequency region. The sign change of stress optical coefficient was shown clearly for PS as the frequency increased. On the other hand, the sign of stress optical coefficient over the whole frequency region is always positive for PC. For COC's of different composition, even though the glass transition temperature can vary, the stress optical coefficient of COC's with different composition stays almost constant at two extremes
Inki Kim(김인기),Beomjun Kim(김범준),Sunghee Woo(우성희),Jeonghwan Gwak(곽정환) 한국컴퓨터정보학회 2022 韓國컴퓨터情報學會論文誌 Vol.27 No.3
본 논문에서는 기존의 스마트폰 카메라 센서를 사용하여 비접촉식 손바닥 기반 사용자 식별 시스템을 구축하기 위해 Attention U-Net 모델과 사전 훈련된 컨볼루션 신경망(CNN)이 있는 다채널 손바닥 이미지를 이용한 앙상블 모델을 제안한다. Attention U-Net 모델은 손바닥(손가락 포함), 손바닥(손바닥 미포함) 및 손금을 포함한 관심 영역을 추출하는 데 사용되며, 이는 앙상블 분류기로 입력되는 멀티채널 이미지를 생성하기 위해 결합 된다. 생성된 데이터는 제안된 손바닥 정보 기반 사용자 식별 시스템에 입력되며 사전 훈련된 CNN 모델 3개를 앙상블 한 분류기를 사용하여 클래스를 예측한다. 제안된 모델은 각각 98.60%, 98.61%, 98.61%, 98.61%의 분류 정확도, 정밀도, 재현율, F1-Score를 달성할 수 있음을 입증하며, 이는 저렴한 이미지 센서를 사용하고 있음에도 불구하고 제안된 모델이 효과적이라는 것을 나타낸다. 본 논문에서 제안하는 모델은 COVID-19 펜데믹 상황에서 기존 시스템에 비하여 높은 안전성과 신뢰성으로 대안이 될 수 있다. In this paper, we propose an ensemble model facilitated by multi-channel palm images with attention U-Net models and pretrained convolutional neural networks (CNNs) for establishing a contactless palm-based user identification system using conventional inexpensive camera sensors. Attention U-Net models are used to extract the areas of interest including hands (i.e., with fingers), palms (i.e., without fingers) and palm lines, which are combined to generate three channels being ped into the ensemble classifier. Then, the proposed palm information-based user identification system predicts the class using the classifier ensemble with three outperforming pre-trained CNN models. The proposed model demonstrates that the proposed model could achieve the classification accuracy, precision, recall, F1-score of 98.60%, 98.61%, 98.61%, 98.61% respectively, which indicate that the proposed model is effective even though we are using very cheap and inexpensive image sensors. We believe that in this COVID-19 pandemic circumstances, the proposed palm-based contactless user identification system can be an alternative, with high safety and reliability, compared with currently overwhelming contact-based systems.