비대칭적 문장접속: 담화문법 하에서 독일어 분석

In this paper, I have showed the possibility to analyze asymmetric coordinate sentences of German within a formal discourse grammar. In section 2, I have surveyed that asymmetric coordinate sentences behave themselves in the syntactic and semantic aspects differently from symmetric coordinate sentences, although they have both the coordinate conjunction und. In section 3, I have seen the so-called Discourse Structure Grammar' of Lee (2001) on which the analyse of this paper are based. Specially, 1 have accepted the concept discourse relations' of Sanders et al. (1992; 1993) and have postulated the hierarchy of discourse relations according to the three dimensions such as operation, polarity and source.이 논문은 1999년 한국학술진흥재단의 연구비에 의하여 연구되었음. (KRF-1999-037-A0012

On the ellipsis of noun phrases in German

In this paper, I have handled NP-ellipses which are supposed to result from the principle of economy. After criticizing two previous approaches, i.e. a deletion- and a copying approach, I have presented a new approach, which makes elliptical phrases interpret as perfect propositions. This so-called default-inheritance system, the concept of which is borrowed from the self-repairing and Know-edge Representation, consists of a hierarchically structured re-presentation and a default-inheritance mechanism. According to the system. an elliptical sentence is structured under a normal sentence. and the highest ranked sentence inherits information to the lower ranked defective sentence. So, an elliptical sentence is interpreted as a combination of the inherited and the inherent information. On the ground of the formalized default-inheritance system, I have found the constraint which works on the NP-ellipsis in German. That is, the element to be inherited must correspond to the most prominent referent in discourses. By means of the interaction between thc system and the constraint, I have explained the various phenomena of German

머리 장착형 화면을 위한 합성곱 신경망을 활용한 근적외선 기반 시선 추적 방법

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적응형 채널 어텐션 모듈을 활용한 복합 열화 복원 네트워크

자율 주행 자동차나 소방 로봇과 같은 시스템에서 영상을 얻을 때 다양한 요인들로 인해 잡음, 블러와 같은 열화가 발생한다. 이런 열화된 영상에 직접 영상 분류와 같은 기술을 적용하기 어려워 열화 제거가 불가피하나 이러한 시스템들은 영상의 열화를 인식할 수 없어서 열화된 영상을 복원하는데 어려움이있다. 본 논문에서는 영상에 적용된 열화를 인지하지 못하는 상황에서 여러 방법들로 열화된 영상으로부터 자연스럽고 선명한 영상을 복원하는 방법을 제안한다. 우리가 제안한 방법은 딥러닝 모델에 채널 어텐션 모듈과 스킵 커넥션을 사용하여 영상에 적용된 열화에 따라 복원에 필요한 채널에 높은 가중치를 적용해복합열화영상의복원을진행한다.이방법은다른복합열화복원방법에비해학습이간단하고기존의다른방법들에 비해 높은 복합 열화 복원 성능을 낸다. The image obtained from systems such as autonomous driving cars or fire-fighting robots often suffer from several degradation such as noise, motion blur, and compression artifact due to multiple factor. It is difficult to apply image recognition to these degraded images, then the image restoration is essential. However, these systems cannot recognize what kind of degradation and thus there are difficulty restoring the images. In this paper, we propose the deep neural network, which restore natural images from images degraded in several ways such as noise, blur and JPEG compression in situations where the distortion applied to images is not recognized. We adopt the channel attention modules and skip connections in the proposed method, which makes the network focus on valuable information to image restoration. The proposed method is simpler to train than other methods, and experimental results show that the proposed method outperforms existing state-of-the-art methods.22Nkc

디블러링 알고리즘의 평가 및 학습을 위한 실제 블러 영상 데이터셋

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