• 유통과학연구
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• 제14권1호
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• pp.93-102
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• 2016

Purpose - The purpose of paper is studying the static and dynamic side for long-term memory storage properties, and increase the explanatory power regarding the long-term memory process by looking at the long-term storage attributes, Korea Composite Stock Price Index. The reason for the use of GPH statistic is to derive the modified statistic Korea's stock market, and to research a process of long-term memory. Research design, data, and methodology - Level shifts were subjected to be an empirical analysis by applying the GPH method. It has been modified by taking into account the daily log return of the Korea Composite Stock Price Index a. The Data, used for the stock market to analyze whether deciding the action by the long-term memory process, yield daily stock price index of the Korea Composite Stock Price Index and the rate of return a log. The studies were proceeded with long-term memory and long-term semiparametric method in deriving the long-term memory estimators. Chapter 2 examines the leading research, and Chapter 3 describes the long-term memory processes and estimation methods. GPH statistics induced modifications of statistics and discussed Whittle statistic. Chapter 4 used Korea Composite Stock Price Index to estimate the long-term memory process parameters. Chapter 6 presents the conclusions and implications. Results - If the price of the time series is generated by the abnormal process, it may be located in long-term memory by a time series. However, test results by price fixed GPH method is not followed by long-term memory process or fractional differential process. In the case of the time-series level shift, the present test method for a long-term memory processes has a considerable amount of bias, and there exists a structural change in the stock distribution market. This structural change has implications in level shift. Stratum level shift assays are not considered as shifted strata. They exist distinctly in the stock secondary market as bias, and are presented in the test statistic of non-long-term memory process. It also generates an error as a long-term memory that could lead to false results. Conclusions - Changes in long-term memory characteristics associated with level shift present the following two suggestions. One, if any impact outside is flowed for a long period of time, we can know that the long-term memory processes have characteristic of the average return gradually. When the investor makes an investment, the same reasoning applies to him in the light of the characteristics of the long-term memory. It is suggested that when investors make decisions on investment, it is necessary to consider the characters of the long-term storage in reference with causing investors to increase the uncertainty and potential. The other one is the thing which must be considered variously according to time-series. The research for price-earnings ratio and investment risk should be composed of the long-term memory characters, and it would have more predictability.

• 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.

• 수면정신생리
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• 제4권1호
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• pp.1-14
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• 1997

This paper reviewed models to explain memory and neuropsychological tests to assess memory. Memory was explained in cognitive and neuroanatomical perspectives, Cognitive model describes memory as structure and process. In structure model, memory is divided into three systems: sensory memory, short-term memory(working memory), and long-term memory. In process model, there are broadly three categories of memory process: encoding, storage, and retrieval. Memory process work in memory structure. There are two prominent models of the neuroanatomy of memory, derived from the work of Mishkin and Appenzeller and that of Squire and Zola-Morgan. These two models are the most useful for the clinician in part because they take into account the connections between the limbic and frontal cortical regions. The major difference between the two models concerns the role of the amygdala in memory processess. Mishkin and his colleagues believe that the amygdala plays a significant role while Squire and his colleagues do not. The most popular and widely used tests of memory ability such as WMS-R, AVLT, CVLT, HVLT. RBMT, CFT, and BVRT-R, were reviewed.

• ETRI Journal
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• 제43권2호
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• pp.288-298
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• 2021

Abstractive text summarization is a process of making a summary of a given text by paraphrasing the facts of the text while keeping the meaning intact. The manmade summary generation process is laborious and time-consuming. We present here a summary generation model that is based on multilayered attentional peephole convolutional long short-term memory (MAPCoL; LSTM) in order to extract abstractive summaries of large text in an automated manner. We added the concept of attention in a peephole convolutional LSTM to improve the overall quality of a summary by giving weights to important parts of the source text during training. We evaluated the performance with regard to semantic coherence of our MAPCoL model over a popular dataset named CNN/Daily Mail, and found that MAPCoL outperformed other traditional LSTM-based models. We found improvements in the performance of MAPCoL in different internal settings when compared to state-of-the-art models of abstractive text summarization.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.577-594
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• 2002

For the estimation and test of long memory feature in volatilities of stock indices and individual companies semiparametric approach, Geweke and Porter-Hudak (1983), is employed. Empirical study supports the strong evidence of volatility persistence in Korean stock market. Most of indices and individual companies have the feature of long term dependence of volatility. Hence the short memory models are unable to explain the volatilities in Korean stock market.

• 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.

• 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.

• 한국음향학회지
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• 제39권2호
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• pp.137-142
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• 2020

문장 종속 짧은 발화에서 문장 독립 긴 발화까지 다양한 환경에서 I-vector 특징에 기반을 둔 많은 연구가 수행되었다. 본 논문에서는 원거리 잡음 환경에서 녹음한 데이터에서 Probabilistic Linear Discriminant Analysis(PLDA)를 적용한 I-vector와 주의 집중 기법을 접목한 Long Short Term Memory(LSTM) 기반의 화자 임베딩을 추출하여 결합한 화자 검증 알고리즘을 소개한다. LSTM 모델의 Equal Error Rate(EER)이 15.52 %, Attention-LSTM 모델이 8.46 %로 7.06 % 성능이 향상되었다. 이로써 본 논문에서 제안한 기법이 임베딩을 휴리스틱 하게 정의하여 사용하는 기존 추출방법의 문제점을 해결할 수 있는 것을 확인하였다. PLDA를 적용한 I-vector의 EER이 6.18 %로 결합 전 가장 좋은 성능을 보였다. Attention-LSTM 기반 임베딩과 결합하였을 때 EER이 2.57 %로 기존보다 3.61 % 감소하여 상대적으로 58.41 % 성능이 향상되었다.

• The Korean Journal of Physiology and Pharmacology
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• 제18권6호
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• pp.457-460
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• 2014

At central synapses, activity-dependent synaptic plasticity has a crucial role in information processing, storage, learning, and memory under both physiological and pathological conditions. One widely accepted model of learning mechanism and information processing in the brain is Hebbian Plasticity: long-term potentiation (LTP) and long-term depression (LTD). LTP and LTD are respectively activity-dependent enhancement and reduction in the efficacy of the synapses, which are rapid and synapse-specific processes. A number of recent studies have a strong focal point on the critical importance of another distinct form of synaptic plasticity, non-Hebbian plasticity. Non-Hebbian plasticity dynamically adjusts synaptic strength to maintain stability. This process may be very slow and occur cell-widely. By putting them all together, this mini review defines an important conceptual difference between Hebbian and non-Hebbian plasticity.

• 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.