• The Transactions of the Korean Institute of Electrical Engineers C
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• v.55 no.8
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.189-192
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• 2021

This study applies a deep learning-based long short-term memory(LSTM) model to track tracking technology. In the case of existing track tracking technology, the weight of constant velocity, constant acceleration, stiff turn, and circular(3D) flight is automatically changed when tracking track in real time using LMIPDA based on Kalman filter according to flight characteristics of an aircraft such as constant velocity, constant acceleration, stiff turn, and circular(3D) flight. In this process, it is necessary to improve performance of changing flight characteristic weight, because changing flight characteristics such as stiff turn flight during constant velocity flight could incur the loss of track and decreasing of the tracking performance. This study is for improving track tracking performance by predicting the change of flight characteristics in advance and changing flight characteristic weigh rapidly. To get this result, this study makes deep learning-based Long Short-Term Memory(LSTM) model study the plot and target of simulator applied with radar error model, and compares the flight tracking results of using Kalman filter with those of deep learning-based Long Short-Term memory(LSTM) model.

• Environmental and Resource Economics Review
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• v.9 no.3
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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.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.395-406
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• 2022

This paper studies the outlier detection method for multivariate long memory time series. The existing outlier detection methods are based on a short memory VARMA model, so they are not suitable for multivariate long memory time series. It is because higher order of autoregressive model is necessary to account for long memory, however, it can also induce estimation instability as the number of parameter increases. To resolve this issue, we propose outlier detection methods based on the VHAR structure. We also adapt the robust estimation method to estimate VHAR coefficients more efficiently. Our simulation results show that our proposed method performs well in detecting outliers in multivariate long memory time series. Empirical analysis with stock index shows RVHAR model finds additional outliers that existing model does not detect.

• Journal of Computing Science and Engineering
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• v.8 no.2
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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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.4
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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.

• Electrical & Electronic Materials
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• v.13 no.12
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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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.

• Journal of Information Technology Services
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• v.16 no.4
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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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.2
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• pp.15-21
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• 2014

The long memory properties of the hedge ratio for stock and futures have not been systematically investigated by the extant literature. To investigate hedge ratio' long memory, this paper employs a data set including KOSPI200 and S&P500. Coakley, Dollery, and Kellard(2008) employ a data set including a stock index and commodities foreign exchange, and suggested the S&P500 to be a fractionally integrated process. This paper firstly estimates hedge ratios with two dynamic models, BEKK(Bollerslev, Engle, Kroner, and Kraft) and diagonal-BEKK, and tests the long memory of hedge ratios with Geweke and Porter-Hudak(1983)(henceforth GPH) and Lo's modified rescaled adjusted range test by Lo(1991). In empirical results, two hedge ratios based on KOSPI200 and S&P500 show considerably significant long memory behaviours. Thus, such results show the hedge ratios to be stationary and strongly reject the random walk hypothesis on hedge ratios, which violates the efficient market hypothesis.