• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.887-890
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• 1998

We present a method for initialization of asynchronous circuits using binary decision space representation. From state transition graph(STG) which is given as a specification a circuit, the BDD is generated to solve the state space explosion problem which is caused by concurrecy of STG. We first step, we construct the necessary informaton as a form of K-map from BDD, then find an initial state on the K-map by assignment of don't care assignment.

• Proceedings of the Korean Nuclear Society Conference
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• 2001.10a
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• pp.80.2-80
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• 2001

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.418-426
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• 2022

Unlike OSR(observation space representation), the SSR(state space representation) augmentation system is suitable for a one-way broadcasting service because it provides the same corrections to all users in the service area. Due to this advantage, several GNSS(global navigation system) systems such as Galileo, BDS(beidou navigation satellite system), QZSS(quasi zenith satellite system) are establishing PPP (precise point positioning)/PPP-RTK precision positioning services based on SSR messages. Therefore, in this paper, we try to understand the trends of satellite-based PPP/PPP-RTK correction services by analyzing the system configurations, characteristics, and precise positioning performance of satellite-based SSR correction broadcasting services.

• Journal of Science of Art and Design
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• v.2
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• pp.193-221
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• 2000

The postmodern culture since the later 20C breaks downa tradition a relation between the reality and languages or sign images expressing it. It develops in the way to review the meaning on the object's imitation or the representation to have been followed since Plato and represent the new state and concept of expressed things. Also, The visual art leads an change of paradigm by images giving up the visual resemblance or the function of representation and endowing them with the new sense. This essay has a purpose to study an important discussion about this change centered on Baudrillard and Lacan. A sociologist Baudrillard promotes the concept of 'simulation' through detecting the reality and the social and historical state of the image. Studying on the course of this change, he calls the step that the image escapes from the stage to reflect the reality and become the pure imitation by itself simulation. The image in the stage of simulation is called 'hyperreality' because it doesn't have any an indicator or a substitute and happens by models without the original or the reality. So he asserts that art is not to contain some absoluteness or transcendency as the past, but to be as the spectacle with characteristics of meaningless, emptiness, contingency. Lacan dismantles the concept of the absolute Cogito to have become the center of the western ideology, and creates the concept of 'Other'. He concludes also the reality exists but can't be captured, and it's impossible for the thinking subject can reach it. The concept of new image which can be thought as the Symbolic in Lacan is 'Signifier without Signified' since it isn't possible to be the transcendent Signifier fixing the meaning finally in it. His 'Gaze' theory is which to be emitted in other's area determines the subject. Equally Baudrillard and Lacan sets up the new state of the image through the end of representation system As for Baudrillard, art intends to the worthlessness and is nothing but imagination. But in Lacan a picture represents the subject being in process by the dialectic of desire.

• Korean Journal of Cognitive Science
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• v.23 no.2
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• pp.133-164
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• 2012

Paul Churchland(1989) suggests the theory of representation from the results of cognitive biology and connectionist AI studies. According to the theory, our representations of the diverse phenomena in the world can be represented as the positions of phase state spaces with the actions of the neurons or of the assembly of neurons. He insists connectionist AI neural networks can have the semantical category systems to recognize the world. But Fodor and Lepore(1996) don't look the perspective bright. From their points of view, the Churchland's theory of representation stands on the base of Quine's holism, and the network semantics cannot explain how the criteria of semantical content similarity could be possible, and so cannot the theory. This thesis aims to excavate which one is the better between the perspective of the theory and the one of Fodor and Lepore's. From my understandings of state space theory of representation, artificial nets can coordinates the criteria of contents similarity by the learning algorithm. On the basis of these, I can see that Fodor and Lepore's points cannot penetrate the Churchlands' theory. From the view point of the theory, we can see how the future's artificial systems can have the conceptual systems recognizing the world. Therefore we can have the perspectives what cognitive scientists have to focus on.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.9-13
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• 2016

Stack LSTM기반 의존 파싱은 전이 기반 파싱에서 스택과 버퍼의 내용을 Stack LSTM으로 인코딩하여 이들을 조합하여 파서 상태 벡터(parser state representation)를 유도해 낸후 다음 전이 액션을 결정하는 방식이다. Stack LSTM기반 의존 파싱에서는 버퍼 초기화를 위해 단어 표상 (word representation) 방식이 중요한데, 한국어와 같이 형태적으로 복잡한 언어 (morphologically rich language)의 경우에는 무수히 많은 단어가 파생될 수 있어 이들 언어에 대해 단어 임베딩 벡터를 직접적으로 얻는 방식에는 한계가 있다. 본 논문에서는 Stack LSTM 을 한국어 의존 파싱에 적용하기 위해 음절-태그과 형태소의 표상들을 결합 (hybrid)하여 단어 표상을 얻어내는 합성 방법을 제안한다. Sejong 테스트셋에서 실험 결과, 제안 단어표상 방법은 음절-태그 및 형태소를 이용한 방법을 더욱 개선시켜 UAS 93.65% (Rigid평가셋에서는 90.44%)의 우수한 성능을 보여주었다.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.615-617
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• 2000

Temporal Logic Frameworks is convenient to represent temporal relation. It is useful to represent a dynamic properties of Discrete Event Dynamic Systems. Also, it is convenient to express a current and next state of event using logical representation. Because the teachability tree of the Temporal Logic Frameworks is very complexity it is difficult to understand. In this paper, we defined some rules to represent Temporal Logic Frameworks by Petri Net and proposed am method of the representation of them Petri Net for the Temporal Logic Frameworks. An example are used to demonstrate the feasibility of this method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1463-1480
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• 2014

It is well known that sparse code is effective for feature extraction of face recognition, especially sparse mode can be learned in the kernel space, and obtain better performance. Some recent algorithms made use of single kernel in the sparse mode, but this didn't make full use of the kernel information. The key issue is how to select the suitable kernel weights, and combine the selected kernels. In this paper, we propose a novel multiple kernel sparse representation based classification for face recognition (MKSRC), which performs sparse code and dictionary learning in the multiple kernel space. Initially, several possible kernels are combined and the sparse coefficient is computed, then the kernel weights can be obtained by the sparse coefficient. Finally convergence makes the kernel weights optimal. The experiments results show that our algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithms.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.9-13
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• 2016

Stack LSTM기반 의존 파싱은 전이 기반 파싱에서 스택과 버퍼의 내용을 Stack LSTM으로 인코딩하여 이들을 조합하여 파서 상태 벡터(parser state representation)를 유도해 낸후 다음 전이 액션을 결정하는 방식이다. Stack LSTM기반 의존 파싱에서는 버퍼 초기화를 위해 단어 표상 (word representation) 방식이 중요한데, 한국어와 같이 형태적으로 복잡한 언어 (morphologically rich language)의 경우에는 무수히 많은 단어가 파생될 수 있어 이들 언어에 대해 단어 임베딩 벡터를 직접적으로 얻는 방식에는 한계가 있다. 본 논문에서는 Stack LSTM 을 한국어 의존 파싱에 적용하기 위해 음절-태그과 형태소의 표상들을 결합 (hybrid)하여 단어 표상을 얻어내는 합성 방법을 제안한다. Sejong 테스트셋에서 실험 결과, 제안 단어 표상 방법은 음절-태그 및 형태소를 이용한 방법을 더욱 개선시켜 UAS 93.65% (Rigid평가셋에서는 90.44%)의 우수한 성능을 보여주었다.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.758-768
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• 2017

In this paper, we present a novel method to extract face representation based on multi-resolution spatial pyramid. In our method, a face is subdivided into increasingly finer sub-regions (local regions) and represented at multiple levels of histogram representations. To cope with misaligned problem, patch-based local descriptor extraction has been also developed in a novel way. To preserve multiple levels of detail in local characteristics and also encode holistic spatial configuration, histograms from all levels of spatial pyramid are integrated by using dimensionality reduction and feature combination, leading to our spatial-pyramid face feature representation. We incorporate our proposed face features into general face recognition pipeline and achieve state-of-the-art results on challenging face recognition problems.