• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제55권8호
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• pp.399-403
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• 2006

An accurate constant output voltage provided by the analog memory cell may be used by the low power oscillator to generate an accurate low frequency output signal. This accurate low frequency output signal may be used to maintain long-term timing accuracy in host devices during sleep modes of operation when an external crystal is not available to provide a clock signal. Further, incorporation of the analog memory cell in the low power oscillator is fully implementable in a 0.35um Samsung standard CMOS process. Therefore, the analog memory cell incorporated into the low power oscillator avoids the previous problems in a oscillator by providing a temperature-stable, low power consumption, size-efficient method for generating an accurate reference clock signal that can be used to support long sleep mode operation.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.189-192
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• 2021

항적추적 기술에 딥러닝 기반 LSTM(Long Short-Term Memory) 모델을 적용하는 연구로서 기존의 항적추적기술의 경우, 항공기의 등속, 등가속, 급기동, 선회(3D) 비행 등 비행 특성에 따른 칼만 필터 기반의 LMIPDA를 활용한 실시간 항적 추적 시 등속, 등가속, 급기동, 선회(3D) 비행 가중치가 자동으로 변경된다. 이러한 과정에서 등속 비행 중 급기동 비행과 같이 비행 특성이 변경될 때, 항적 손실 및 항적 추적 성능이 하락하여 비행 특성 가중치 변경성능을 향상시킬 필요성이 있다. 본 연구는 레이더의 오차 모델이 적용된 시뮬레이터의 Plot과 표적을 딥러닝 기반 LSTM(Long Short-Term Memory) 모델을 적용하여 학습시키고, 칼만 필터를 활용한 항적추적 결과와 딥러닝 기반 LSTM(Long Short-Term Memory) 모델을 적용한 항적추적결과를 비교함으로써 미리 비행 특성의 변경과정을 예측하여 등속, 등가속, 급기동, 선회(3D) 비행 가중치변경을 신속하게 함으로써 항적추적성능을 향상하기 위한 연구이다.

• 자원ㆍ환경경제연구
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• 제9권3호
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• pp.515-544
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• 2000

This paper shows that various fractionally integrated univariate and multivariate are remarkably successful in representing annual temperature series and also very long series of tree ring widths, which are often used as a proxy for temperature. The analysis also suggests that human recorded temperature series are not inconsistent with being generated by a stationary, long memory process. From the empirical results, we should be noted that the statistically significant positive trend coefficients may well be due to small sample sizes. These results cast some doubt on the basic assumption that global warming is definitely occurring.

• 응용통계연구
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• 제35권3호
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• pp.395-406
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• 2022

본 논문에서는 장기 종속 다변량 시계열 자료에 대한 이상점 탐지 기법을 연구한다. 기존 다변량 시계열 이상점 탐지 방법은 단기 종속 시계열 모형인 VARMA에 기반한 방법으로, 장기억성을 띈 다변량 시계열 자료에는 적합하지 않다. 자기회귀 모형을 통해서 장기 종속성, 즉 장기억성을 고려하기 위해서는 높은 차수의 모형이 필요하고, 이는 곧 추정의 불안성으로 이어지기에 장기억성을 효율적으로 다룰 수 없기 때문이다. 따라서, 본 논문은 이러한 문제를 보완하고자 VHAR 구조에 기반한 이상점 탐지 방법을 제시하고자 한다. 또한 더욱 정확한 추론을 위해서 로버스트한 방법을 이용하여 VHAR 계수를 추정하였고 이를 활용하여 이상점을 탐지하였다. 모의실험 결과 우리가 제안한 방법론이 기존 VARMA에 기반한 방법론보다 이상점 탐지에 더 효과적임을 살펴볼 수 있었다. 주가지수에 대한 실증자료 분석에서도 기존의 방법론은 탐지하지 못하는 추가 이상점을 찾음을 확인할 수 있었다.

• Journal of Computing Science and Engineering
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• 제8권2호
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• pp.78-86
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• 2014

Aiming to solve the problems of high memory access and big storage space and long matching time in the regular expression matching of extended finite automaton (XFA), a new regular expression matching algorithm based on high-efficient finite automaton is presented in this paper. The basic idea of the new algorithm is that some extra judging instruments are added at the starting state in order to reduce any unnecessary transition paths as well as to eliminate any unnecessary state transitions. Consequently, the problems of high memory access consumption and big storage space and long matching time during the regular expression matching process of XFA can be efficiently improved. The simulation results convey that our proposed scheme can lower approximately 40% memory access, save about 45% storage space consumption, and reduce about 12% matching time during the same regular expression matching process compared with XFA, but without degrading the matching quality.

• 디지털산업정보학회논문지
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• 제5권4호
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• pp.169-177
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• 2009

In come cases, people differently respond on the same joke or thoughtless behavior - sometimes like it and laugh, another time feel annoyed or angry. This fact is explained that experiences which we had in the past are remembered by emotional memory, so they cause different responses. When people face similar situation or feel similar emotion, they evoke the emotion experienced in the past and the emotional memory affects current emotion. This paper suggested the mechanism of the emotional memory using SOM through the similarity between the emotional memory and SOM learning algorithm. It was assumed that the mechanism of the emotional memory has also the characteristics of association memory, long-term memory and short-term memory in its process of remembering emotional experience, which are known as the characteristics of the process of remembering factual experience. And then these characteristics were applied. The mechanism of the emotional memory designed like this was applied to toy hammer game and I measured the change in the power of toy hammer caused by differently responding on the same stimulus. The mechanism of the emotional memory suggest in above is expected to apply to the fields of game, robot engineering, because the mechanism can express various emotions on the same stimulus.

• E2M - 전기 전자와 첨단 소재
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• 제13권12호
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• pp.6-9
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• 2000

Over the years of decades, the memory technology has progressed a long, marble way. As we have evidenced from the Intel's 1Kb DRAM in 1970 to the Gigabit era of 2000's, the road further ahead towards the Terabit era will be unfolded. The technology once perceived inconceivable is in realization today, and similarly roadblocks as we know of today mayvecome trivial issues for tomorrow. For the inquiring mind, the question is how the "puzzle"of tomorrow's memory technology is pieced-in today. The process will take place both in evolutionary and revolutionary ways. Among these, note-worthy are the changes in DRAM architecture and the cell process technology. In this paper, some technical approaches will be discussed to bring these aspects into a general overview and a per-spective with possibilities for the new memory technology will be presented.presented.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 하계학술대회 논문집
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• pp.17-20
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• 2000

Over the years of decades, the memory technology has progressed a long, marble way. As we have evidenced from the Intel’s 1Kb DRAM in 1970 to the Gigabit era of 2000’s, the road further ahead towards the Terabit era will be unfolded. The technology once perceived inconceivable is in realization today, and similarly roadblocks as we know of today may become trivial issues for tomorrow. For the inquiring mind, the question is how the “puzzle” of tomorrow’s memory technology is pieced-in today. The process will take place both in evolutionary and revolutionary ways. Among these, note-worthy are the changes in DRAM architecture and the cell process technology. In this paper, some technical approaches will be discussed to bring these aspects into a general overview and a perspective with possibilities for the new memory technology will be presented.

• 한국IT서비스학회지
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• 제16권4호
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• pp.149-160
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• 2017

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker's emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

• 벤처창업연구
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• 제9권2호
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• pp.15-21
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• 2014

주식과 선물간의 헤지비율의 시계열 안정성에 대한 연구는 아직 찾아보기 어렵다. 본 연구는 KOSPI200과 S&P500의 주식과 선물 지수를 이용하여 한국과 미국, 두 금융시장의 헤지비율에 대한 시계열 안정성을 연구한다. Coakley, Dollery, and Kellard(2008)는 1995년부터 2005년의 S&P500 현물을 대상으로 시계열 안정성을 확인하였다. 본 연구는 선행연구에서 시계열 안정성이 검증된 기간을 분석기간에 포함하여 두 시장을 분석함으로써 연구결과의 강건성을 얻고자 한다. 한국시장의 분석기간은 주식선물시장이 개설된 1996년부터 2005년이다. S&P500은 1982년부터 2004년을 분석대상으로 하고 있다. 본 연구는 BEKK and diagonal-BEKK을 사용하여 헤지비율을 구하며, 시계열 안정성 검증을 위하여 R/S와 GPH 방법을 사용한다. 분석결과는 시장효율성의 이론적 근거가 되는 랜덤워크가설을 지지하지 않는다. 이 결과는 헤지비율을 이용한 위험관리 방안에 대한 시사점을 제공한다.