• 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.

• 동의생리병리학회지
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• 제17권3호
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• pp.684-692
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• 2003

The herbal extract(YMT_02) is a modified extracts from Yukmijihwang-tang(YMJ) to promote memory-enhancing. The YMJ extracts has been widely used as replenishing yin and tonifying the kidneys herbal medicine for hundred years ia Asian countries. The purpose of this study is to: 1) quantitatively evaluate the memory-enhancing effect of YMT_02 by passive avoidance test, 2) statistical evaluation of candidate gene expression (pentraxin. PEP-19, transthyretin) in rat hippocampus. The hippocampi of YMT_02 and control group were dissected and mRNA was further purified. After synthesizing cDNA using oligo-dT primer, the cDNA were applied to Real Time PCR. The results were as follows : 1) passive avoidance test showed enhancing memory retentin by YMT_02 treatment, 2) expression of pentraxin, that accelerate degenerating of neuronal cell, was significantly decreased, 3) the mRNA of genes that has been known to be associated with protecting neuronal cell degeneration, such as PEP-19 and transthyretin, were significantly increased upon YMT_02 treatment. From above results, the administration of YMT_02 which tonify the function of Kidneys could enhance the ability of memory and learning. In addition, the administration of YMT_02 enhance memory retention through modulating particular gene (pentraxin, PEP-19, transthyretin) expressions in hippocampu.

• BMB Reports
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• 제49권12호
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• pp.671-680
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• 2016

Accumulating evidence indicates many brain functions are mediated by epigenetic regulation of neural genes, and their dysregulations result in neuronal disorders. Experiences such as learning and recall, as well as physical exercise, induce neuronal activation through epigenetic modifications and by changing the noncoding RNA profiles. Animal models, brain samples from patients, and the development of diverse analytical methods have broadened our understanding of epigenetic regulation in the brain. Diverse and specific epigenetic changes are suggested to correlate with neuronal development, learning and memory, aging and age-related neuronal diseases. Although the results show some discrepancies, a careful comparison of the data (including methods, regions and conditions examined) would clarify the problems confronted in understanding epigenetic regulation in the brain.

• 수면정신생리
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• 제4권1호
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• pp.29-38
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• 1997

치매는 기억력을 포함한 다양한 영역의 인지기능의 손상을 특징으로 하는 질병군으로 정의된다. 그러므로 현재 치매진단을 위하여 사용되고 있는 대부분의 진단기준들은 치매의 진단에 있어 기억 장애를 필수요소로 포함하고 있다. 기억력의 감퇴는 노화과정의 결과로서 나타날 수 있다. 치매와는 달리 노화에서의 기억력의 감퇴는 노화에 따른 정상으로 간주되며 사회적 그리고 직업적 기능의 영역에서 심각한 어려움이나 손상을 초래하지 않는다. 우울증 또한 기억장애를 동반할 수 있다. 하지만 치매와는 달리 우울증에서는 언어성 지연회상과 재인기억의 감소는 나타내지 않는다. 치매환자들에서의 기억장애도 병소의 위치에 따라 다른양상으로 나타날 수 있다. 피질성치매에서의 기억장애는 정보의 부호화와 기억강화과정의 이상에 의해 초래된 것인데, 피질하치매에서의 기억장애는 인출의 장애에 의한 것이다.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권4호
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• pp.268-273
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• 2017

최근 감정 분류 분야에서 딥러닝 인코더 기반의 접근 방법이 활발히 적용되고 있다. 딥러닝 인코더 기반의 접근 방법은 가변 길이 문장을 고정 길이 문서 벡터로 압축하여 표현한다. 하지만 딥러닝 인코더에 흔히 사용되는 구조인 장 단기 기억망(Long Short-Term Memory network) 딥러닝 인코더는 문서가 길어지는 경우, 문서 벡터 표현의 품질이 저하된다고 알려져 있다. 본 논문에서는 효과적인 감정 문서의 분류를 위해, 장 단기 기억망의 출력을 중요도에 따라 가중합하여 문서 벡터 표현을 생성하는 주목방법 기반의 딥러닝 인코더를 사용하는 것을 제안한다. 또한, 주목 방법 기반의 딥러닝 인코더를 문서의 감정 분류 영역에 맞게 수정하는 방법을 제안한다. 제안하는 방법은 윈도우 주목 방법(Window Attention Method)을 적용한 단계와 주목 가중치 재조정(Weight Adjustment) 단계로 구성된다. 윈도우 주목 방법은 한 단어 이상으로 구성된 감정 자질을 효과적으로 인식하기 위해, 윈도우 단위로 가중치를 학습한다. 주목 가중치 재조정에서는 학습된 가중치를 평활화(Smoothing) 한다, 실험 결과, 본 논문에서 제안하는 방법은 정확도 기준으로 89.67%의 성능을 나타내어 장 단기 기억망 인코더보다 높은 성능을 보였다.

• 대한본초학회지
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• 제28권2호
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• pp.25-31
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• 2013

Objectives : The purpose of this study was to characterize the effect of the Ethanolic extracts of Platycodon grandiflorum and its permented production the learning and memory impairments induced by scopolamine. Methods : The cognition-enhancing effect of Platycodon grandiflorum and its permented production were investigated using a passive avoidance test, the Morris water maze test and Y-maze test in mice. Drug-induced amnesia was induced by treating animals with scopolamine (1 mg/kg, i.p.). Results : The results showed that the Permented Platycodon grandiflorum extract-treated group (100 mg/kg, p.o.) and the tacrine-treated group (10 mg/kg, p.o.) significantly ameliorated scopolamine-induced amnesia based on the Passive avoidance Y-maze test and Water maze test. And these results are same manner in DPPH radical scavenger effect and Acetylcholineseterase inhibition effect. Conclusions : These results suggest that 80% Ethanol extract of fermented P.grandiflorum showed significant anti-amnestic and cognitive-enhancing activities related to the memory processes, and these activities were parallel to treatment duration and dependent of the learning models.

• 전자통신동향분석
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• 제38권5호
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• pp.23-33
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• 2023

With the widespread demand from data-intensive tasks such as machine learning and large-scale databases, the amount of data processed in modern computing systems is increasing exponentially. Such data-intensive tasks require large amounts of memory to rapidly process and analyze massive data. However, existing computing system architectures face challenges when building large-scale memory owing to various structural issues such as CPU specifications. Moreover, large-scale memory may cause problems including memory overprovisioning. The Compute Express Link (CXL) allows computing nodes to use large amounts of memory while mitigating related problems. Hence, CXL is attracting great attention in industry and academia. We describe the overarching concepts underlying CXL and explore recent research trends in this technology.

• Communications for Statistical Applications and Methods
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• 제26권5호
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• pp.497-506
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• 2019

Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by McCracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.279-285
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• 2007

The objective of the research is two fold. The first is to design and propose a stable and robust learning control algorithm. The controller is CMAC Learning Controller which consists of a model-based controller, such as LQR or PID, as a reference control and a CMAC. The second objective is to implement a reference control and CMAC at two different sampling rates. Generally, a conventional controller is designed based on a mathematical plant model. However, increasing complexity of the plant and accuracy requirement on mathematical models nearly prohibits the application of the conventional controller design approach. To avoid inherent complexity and unavoidable uncertainty in modeling, biology mimetic methods have been developed. One of such attempts is Cerebellar Model Articulation Computer(CMAC) developed by Albus. CMAC has two main disadvantages. The first disadvantage of CMAC is increasing memory requirement with increasing number of input variables and with increasing accuracy demand. The memory needs can be solved with cheap memories due to recent development of new memory technology. The second disadvantage is a demand for processing powers which could be an obstacle especially when CMAC should be implemented in real-time. To overcome the disadvantages of CMAC, we propose CMAC learning controller with multiple sampling rates. With this approach a conventional controller which is a reference to CMAC at high enough sampling rate but CMAC runs at the processor's unoccupied time. To show efficiency of the proposed method, an inverted pendulum controller is designed and implemented. We also demonstrate it's possibility as an industrial control solution and robustness against a modeling uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.22.3-22
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• 2001

An attention task, in which context information should be extracted from the first presented pattern, and the recognition answer of the second presented pattern should be generated using the context information, is employed in this paper. An Elman-type recurrent neural network is utilized to extract and keep the context information. A reinforcement signal that indicates whether the answer is correct or not, is only a signal that the system can obtain for the learning. Only by this learning, necessary context information became to be extracted and kept, and the system became to generate the correct answers. Furthermore, the function of an associative memory is observed in the feedback loop in the Elman-type neural network.