• Biomolecules & Therapeutics
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• v.22 no.3
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• pp.232-238
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• 2014

Alzheimer's disease (AD) is the most common neurodegenerative disease without known ways to cure. A key neuropathologic manifestation of the disease is extracellular deposition of beta-amyloid peptide (Ab). Specific mechanisms underlying the development of the disease have not yet been fully understood. In this study, we investigated effects of 4-O-methylhonokiol on memory dysfunction in APP/PS1 double transgenic mice. 4-O-methylhonokiol (1 mg/kg for 3 month) significantly reduced deficit in learning and memory of the transgenic mice, as determined by the Morris water maze test and step-through passive avoidance test. Our biochemical analysis suggested that 4-O-methylhonokiol ameliorated $A{\beta}$ accumulation in the cortex and hippocampus via reduction in beta-site APP-cleaving enzyme 1 expression. In addition, 4-O-methylhonokiol attenuated lipid peroxidation and elevated glutathione peroxidase activity in the double transgenic mice brains. Thus, suppressive effects of 4-O-methylhonokiol on $A{\beta}$ generation and oxidative stress in the brains of transgenic mice may be responsible for the enhancement in cognitive function. These results suggest that the natural compound has potential to intervene memory deficit and progressive neurodegeneration in AD patients.

• Journal of Ginseng Research
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• v.20 no.2
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• pp.139-148
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• 1996

This study was designed to examine the effects of red ginseng total saponin and extract on spatial working memory in mice using Morris water maze. Two kinds of red ginseng saponin (No. 1 and No. 2) and three kinds of red ginseng extract (No. 1, No. 2 and No. 3) to have different PD/ PT ratio (No. 1=1.24, No.2=1.47 No.3=2.41) were prepared by mixing the different parts of red ginseng In different ratio. In acute administration of total saponin No. 1 or No. 2, escape time to reach to a hidden platform In a fixed location for training trials was significantly decreased as compared with control group and swimming time in the quadrant that had contained the platform was also significantly increased as compared with control group. In acute treatment of extract No. 1 or 1 No. 2, swimming time in the platformless quadrant was increased dose dependently as compared with control group, especially at dose of 200 mg/kg,bw swimming time was significantly Increased. Oral treatment of extract No. 1 (100 mg/kg, bw) for 7 days produced an increase of swimming time In the platformless quadrant but a decrease of swimming time in No.3-treated group (100 mg/kg, bw). These results show that red ginseng may improve spatial discrimination learning and spatial working memory of mice

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.391-393
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• 2021

In this paper, we introduce a long short-term memory (LSTM)-based fall detection system using TensorFlow that can detect falls occurring in the elderly in daily living. 3-axis accelerometer data are aggregated for fall detection, and then three types of parameter are calculated. 4 types of activity of daily living (ADL) and 3 types of fall situation patterns are classified. The parameterized data applied to LSTM. Learning proceeds until the Loss value becomes 0.5 or less. The results are calculated for each parameter θ, SVM, and GSVM. The best result was GSVM, which showed Sensitivity 98.75%, Specificity 99.68%, and Accuracy 99.28%.

• Journal of Applied Biological Chemistry
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• v.48 no.4
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• pp.207-212
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• 2005

Prolyl endopeptidase (PEP, EC 3.4.21.26), also referred to as prolyl oligopeptidase, has been suggested to participate in learning and memory processes by cleaving peptide bonds on carboxyl side of prolyl residue within neuropeptides of less than 30 amino acids, and is abundant in brains of amnestic patients. Therefore, compounds possessing PEP inhibitory activity can be good candidate of drug against memory loss. Upon examination for PEP inhibition from traditional medicinal plants having tonic, stimulating, and anti-amnestic effects, Corni Fructus (Cornus officinallis) showed significant PEP inhibition. Ursolic and oleanolic acids, components of Corni Fructus, inhibited PEP with $IC_{50}$ values of $17.2\;{\pm}\;0.5$ and $22.5\;{\pm}\;0.7\;{\mu}M$, respectively.

• International Commerce and Information Review
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• v.18 no.3
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• pp.155-179
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• 2016

The rapid changing technology and globalization allow consumers get information and new products or services faster, have more choices than before, which might be causing more competitive and more uncertain demand. The relationship quality between enterprises positively influence the relational performance. Through this research, enterprises should realize the importance of relationship learning to improve the competitive advantage. Also this research provide the strategic solutions to promote the relationship learning. this is considered to be able to present an improved directionality of the relationship between the buyer and the supplier. In addition, from the perspective of policy, this research provides implications for large enterprises and SMEs to promote their coexistence relation. The empirical model of this paper is established on basis of previous research. The empirical results show that: first, as the influence factors, relation solidarity level, environmental uncertainty, learning intension affect relationship learning, whilst special transaction assets influence information shared and relationship memory and have no effect on mutual understanding; second, relationship learning influence on relational performance and this influence relation becomes stronger if the relationship trust is higher.

• Journal of the Microelectronics and Packaging Society
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• v.26 no.1
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• pp.1-7
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• 2019

Recently, memristor (anion-based memristor) is referred to as the fourth circuit element which resistance state can be gradually changed by the electric pulse signals that have been applied to it. And the stored information in a memristor is non-volatile and also the resistance of a memristor can vary, through intermediate states, between high and low resistance states, by tuning the voltage and current. Therefore the memristor can be applied for analogue memory and/or learning device. Usually, memristive behavior is easily observed in the most transition metal oxide system, and it is explained by electrochemical migration motion of anion with electric field, electron scattering and joule heating. This paper reports the latest trends and issues of anion-based memristor.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.329-338
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• 2021

This study predicts the amount of wind power generation for rational operation plan of wind power generation and capacity calculation of ESS. For forecasting, we present a method of predicting wind power generation by combining a physical approach and a statistical approach. The factors of wind power generation are analyzed and variables are selected. By collecting historical data of the selected variables, the amount of wind power generation is predicted using deep learning. The model used is a hybrid model that combines a bidirectional long short term memory (LSTM) and a convolution neural network (CNN) algorithm. To compare the prediction performance, this model is compared with the model and the error which consist of the MLP(:Multi Layer Perceptron) algorithm, The results is presented to evaluate the prediction performance.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.69-81
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• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.199-208
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• 2019

As the number of available satellites increases and technology advances, image information outputs are becoming increasingly diverse and a large amount of data is accumulating. In this study, we propose a change detection method for high-resolution satellite images that uses transfer learning and a deep learning network to overcome the limit caused by insufficient training data via the use of pre-trained information. The deep learning network used in this study comprises convolutional layers to extract the spatial and spectral information and convolutional long-short term memory layers to analyze the time series information. To use the learned information, the two initial convolutional layers of the change detection network are designed to use learned values from 40,000 patches of the ISPRS (International Society for Photogrammertry and Remote Sensing) dataset as initial values. In addition, 2D (2-Dimensional) and 3D (3-dimensional) kernels were used to find the optimized structure for the high-resolution satellite images. The experimental results for the KOMPSAT-3A (KOrean Multi-Purpose SATllite-3A) satellite images show that this change detection method can effectively extract changed/unchanged pixels but is less sensitive to changes due to shadow and relief displacements. In addition, the change detection accuracy of two sites was improved by using 3D kernels. This is because a 3D kernel can consider not only the spatial information but also the spectral information. This study indicates that we can effectively detect changes in high-resolution satellite images using the constructed image information and deep learning network. In future work, a pre-trained change detection network will be applied to newly obtained images to extend the scope of the application.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.236-241
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• 2024

Due to the recent emphasis on carbon neutrality and environmental regulations, the global electric vehicle (EV) market is experiencing rapid growth. This surge has raised concerns about the recycling and disposal methods for EV batteries. Unlike traditional internal combustion engine vehicles, EVs require unique and safe methods for the recovery and disposal of their batteries. In this process, predicting the lifespan of the battery is essential. Impedance and State of Charge (SOC) analysis are commonly used methods for this purpose. However, predicting the lifespan of batteries with complex chemical characteristics through electrical measurements presents significant challenges. To enhance the accuracy and precision of existing measurement methods, this paper proposes using a Long Short-Term Memory (LSTM) model, a type of deep learning-based recurrent neural network, to diagnose battery performance. The goal is to achieve safe classification through this model. The designed structure was evaluated, yielding results with a Mean Absolute Error (MAE) of 0.8451, a Root Mean Square Error (RMSE) of 1.3448, and an accuracy of 0.984, demonstrating excellent performance.