• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.154-162
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• 2003

In this paper, Associative Memory with Cellular Neural Networks classifies and recognizes image patterns as an operator applied to image process. CNN processes nonlinear data in real-time like neural networks, and made by cell which communicates with each other directly through its neighbor cells as the Cellular Automata does. It is applied to the optimization problem, associative memory, pattern recognition, and computer vision. Image processing with CNN is appropriate to 2-D images, because each cell which corresponds to each pixel in the image is simultaneously processed in parallel. This paper shows the method for designing the structure of associative memory based on CNN and getting output image by choosing the most appropriate weight pattern among the whole learned weight pattern memories. Each template represents weight values between cells and updates them by learning. Hebbian rule is used for learning template weights and LMS algorithm is used for classification.

• Journal of Oriental Neuropsychiatry
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• v.14 no.1
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• pp.59-74
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• 2003

Alzheimer's disease(AD) is a progressive neurodegenerative disease, which is pathologically characterized by neuritic plaques and neurofibrillary tangles associated with the acetylcholinesterase, apolipoprotein E and butylcholinesterase, and by mutations in the presenilin genes PS1 and PS2, and amyloid precursor proteins (APP) overexpression. The present research is to examine the inhibition effect of CPRT on PS-1, PS-2 and APP overexpression by detected to Western blotting. To verify the Effects of CPRT on cognitive deficits further, we tested it on the scopolamine-induced amnesia model of the mice using the Morris water maze tests, and there was ameliorative effects of memory impairment as a protection to scopolamine. CPRT only partially blocked the increase in blood serum level of acetylcholinesterase and Uric acid induced by scopolamine, whereas blood glucose level was shown to attenuate the amnesia induced by scopolamine and inreased extracellular serum level compared with only scopolamine injection. In conclusion, studies of CPRT that has been known as anti-choline and inhibition ablilities of APP overexpression, this could also be used further as a important research data for a preventive and promising symptomatic treatment for Alzheimer's disease.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.5
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• pp.876-880
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• 2011

Gammakdaejo-Tang (GMT) is a traditional oriental medicinal formula, a mixture of 3 crude drugs, and it has been clinically used for treating mild depressive disorders. The purpose of the study was to examine the effect of Gammakdaejo-Tang (GMT) on repeated stress-induced alterations of learning and memory on a passive avoidance test (PAT) test and also the anxiety-related behavior on the elevated pulse maze (EPM) in ovariectomized female rats. We assessed the changes in the reactivity of the cholinergic system by measuring the immunoreactive neurons of choline acetyltransferase (ChAT) in the hippocampus after behavioral testing. The rats were exposed to the immobilization (IMO) stress for 14 days (2hours/day), and Gammakdaejo-Tang (400 mg/kg, p.o.) was administered 30 min before IMO stress. Treatments with GMT caused significant reversals of the stress-induced deficits in learning and memory on a working memory test, and it also produced an anxiolytic-like effect on the EPM, and increased the ChAT reactivities (p<0.001, respectively). These results suggest that Gammakdaejo-Tang might prove to be an effective antidepressant agent.

• Korean Journal of Pharmacognosy
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• v.45 no.1
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• pp.48-54
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• 2014

Alzheimer's disease is progressive neurodegenerative disorder by the loss of memory and learning abilities. Codonopsis lanceolata (C. lanceolata) is traditional medicinal plant used for the treatment of inflammatory diseases. The aim of study was to evaluate the effect of steamed C. lanceolata on scopolamine-induced memory impairment in the Morris water maze test and passive avoidance test. In addition, this study investigated the neuroprotective effects of steamed C. lanceolata on glutamate-induced cell death in HT22 cells using MTT assay. The results showed that steamed C. lanceolata (500 mg/kg body weight, p.o.) reversed spatial memory impairment by scopolamine in Morris water maze test and passive avoidance test. Steamed C. lanceolata attenuated memory impairment by scopolamine compared with common C. lanceolata. In addition, administration of steamed C. lanceolata significantly also reduced cell death. We suggest that steaming process more improve cognitive enhancing and neuroprotective effect of C. lanceolata than common C. lanceolata.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.469-476
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• 2022

This study developed a Recurrent Neural Network model implemented through Long Short-Term Memory (LSTM) that generates long-term tidal level data at Busan Port using tide observation data. The tide levels in Busan Port were predicted by the Korea Hydrographic and Oceanographic Administration (KHOA) using the tide data observed at Busan New Port and Tongyeong as model input data. The model was trained for one month in January 2019, and subsequently, the accuracy was calculated for one year from February 2019 to January 2020. The constructed model showed the highest performance with a correlation coefficient of 0.997 and a root mean squared error of 2.69 cm when the tide time series of Busan New Port and Tongyeong were inputted together. The study's finding reveal that long-term tidal level data prediction of an arbitrary port is possible using the deep learning recurrent neural network model.

• Speech Sciences
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• v.8 no.4
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• pp.119-129
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• 2001

Language is one aspect of cognition, along with attention and concentration, learning and memory, visuospatial abilities, and executive function. The purpose of this study was to determine the effect of language intervention by cognitive approach on language expressive performance in a patient with conduction aphasia. This study used several tasks such as Attention and concentration task, visual memory tasks, memory tasks, categorization, divergent thinking, self-monitoring and evaluate thinking. The effects of treatment were evaluated by periodic probing of both trained and untrained familiar words in three tasks; picture naming, answering to questions and telling stories. The results showed improvements both in trained and untrained words. Therefore, we concluded that expressive language performance of this aphasic patient is amenable to this intervention, and that cognitive therapy approach can be useful.

• (The) Korean Journal of Educational Psychology
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• v.31 no.4
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• pp.819-845
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• 2017

Although testing is usually done for purposes of assessment, previous research over the past 100 years indicates that testing is an effective tool for learning. Testing or retrieval practice of previously studied materials can enhance learning of that previously studied information and/or learning of subsequently presented new information. The former is referred to as the backward effect of testing whereas the latter is referred to as the forward effect of testing. Thus far, however, the literature has not isolated these two effects and most previous research focused on the backward effect. Only recent laboratory research provided evidence that there is a forward effect of testing. The present study provides a review of research on this forward and backward effect of testing, focusing on testing procedures of the effects, empirical evidence, current theoretical explanations, and issues to resolve in order to make use of testing effect in educational settings. The reviews clearly show that testing enhances memory of previously learned information by working as memory modifier and learning of newly presented information by affecting learners' metacognition, implying that testing is not just an assessment of learning, but also an effective tool for learning.

• Smart Structures and Systems
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• v.32 no.6
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• pp.359-369
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• 2023

Servo-motor driven uniaxial shake tables have been widely used for education and research purposes in earthquake engineering. These shake tables are mostly displacement-controlled by a digital proportional-integral-derivative (PID) controller; however, accurate reproduction of acceleration time histories is not guaranteed. In this study, a control strategy is proposed and verified for uniaxial shake tables driven by a servo-motor. This strategy incorporates a deep-learning algorithm named Long Short-Term Memory (LSTM) network into a displacement PID feedback controller. The LSTM controller is trained by using a large number of experimental data of a self-made servo-motor driven uniaxial shake table. After the training is completed, the LSTM controller is implemented for directly generating the command voltage for the servo motor to drive the shake table. Meanwhile, a displacement PID controller is tuned and implemented close to the LSTM controller to prevent the shake table from permanent drift. The control strategy is named the LSTM-PID control scheme. Experimental results demonstrate that the proposed LSTM-PID improves the acceleration tracking performance of the uniaxial shake table for both bare condition and loaded condition with a slender specimen.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.123-125
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• 2022

In this paper, we introduce a system applied to long short-term memory using Yolo-pose. Using Yolo-pose from image data, data divided into daily life and falls are extracted and applied to LSTM for learning. In order to prevent overfitting, training is performed 8 to 2 validation and is represented by a confusion matrix. The result of Yolo-pose recorded 100% of both sensitivity and specificity, confirming that daily life and falls were well distinguished.