• Korean Journal of Pharmacognosy
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• v.41 no.3
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• pp.196-203
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• 2010

Black sesame (Sesami semen nigrum) has been used to treat dizziness, earnoise, constipation in the traditional Chinese medicine. In the present study, we assessed memory enhancing properties of 70% ethanolic extract of black sesame (EBS70) and its ameliorating activities on learning and memory impairments induced by scopolamine. Drug-induced amnesia was made by scopolamine treatment (1 mg/kg, i.p.). Single EBS70 (200 mg/kg, p.o.) administration significantly enhanced cognitive function and attenuated scopolamine-induced cognitive impairments as determined by the passive avoidance and Y-maze tasks (P<0.05) and also reduced escape-latency on the Morris water maze task (P<0.05). In addition, EBS70 increased BDNF expression in hippocampus 4 h after its administration (P<0.05). These results suggest that EBS70 enhances learning and memory in normal state and attenuates amnesic state caused by cholinergic dysfunction.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.118-130
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• 1996

Fuzzy inference is very useful in expressing ambiguous problems quantitatively and solving them. But like the most of the knowledge based inference systems. It has many difficulties in constructing rules and no learning capability is available. In this paper, we proposed a fuzzy inference system based on fuzy associative memory to solve such problems. The inference system proposed in this paper is mainly composed of learning phase and inference phase. In the learning phase, the system initializes it's basic structure by determining fuzzy membership functions, and constructs fuzzy rules in the form of weights using learning function of fuzzy associative memory. In the inference phase, the system conducts actual inference using the constructed fuzzy rules. We applied the fuzzy inference system proposed in this paper to a pattern classification problem and show the results in the experiment.

• Journal of Oriental Neuropsychiatry
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• v.9 no.2
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• pp.19-35
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• 1998

Some oriental medicine turned out to have a significant clinical effect on the cure for dementia. Therefore, thorough scientific tests for physiological effect of oriental medicne are needed. This study is aimed at doing experimental studies on the effects of two medicines, Jowiseungcheongtang and hyungbangjihwangtang, on the cure for dementia.For the demonstration of the effect of the two medicines on aged rats, we perfomed a radial arm medicines on aged rats, water maze task, known for their proper learning paradigm for behavior.Previous studies on aging and dementia show that aged rats displayed significant impariments in the learning of the radial arm maze task compared with younger rats. As in experiment 1, we found that the learning of the radial arm maze task compared with younger rats. As in experiment 1, we found that the learning deficits aged rats exhibit in radial arm maze task were improved with the application of each medicine. The resutls suggest that these two medicine can be effective to patients whose working or shortterm memory is impaired. In experiment 2 we studied the effect of the two medicines on the deficit of the aged rats with the Morris water maze task known for measuring long-tern memory. We did not find significant results between the performance of the ages rats and the younger ones. Considered the different results previous studies have reported, more thorough studies are needed to investigate the effect of the medicines on long-term memory.In conclusion, the results we found in experiment 1 and 2 suggest that Jowisengcheongtang and hyungbangjihwangtang can have useful effects for the cure of age-related memory (especially for short-term memory)deficits. Recent interests in dementia urges researchers concerned to explore the effect of oriental medicine on the disease. As there have been relatively few behavioral or scientific studies on dementia using oriental medicine to date, further studies are expected are expected to continue to elucidate 'what the wisdom of the oriental medicine tells about dementia'.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1230-1244
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• 2022

This paper proposes an algorithm for signal validation using unsupervised methods in emergency situations at nuclear power plants (NPPs) when signals are rapidly changing. The algorithm aims to determine the stuck failures of signals in real time based on a variational auto-encoder (VAE), which employs unsupervised learning, and long short-term memory (LSTM). The application of unsupervised learning enables the algorithm to detect a wide range of stuck failures, even those that are not trained. First, this paper discusses the potential failure modes of signals in NPPs and reviews previous studies conducted on signal validation. Then, an algorithm for detecting signal failures is proposed by applying LSTM and VAE. To overcome the typical problems of unsupervised learning processes, such as trainability and performance issues, several optimizations are carried out to select the inputs, determine the hyper-parameters of the network, and establish the thresholds to identify signal failures. Finally, the proposed algorithm is validated and demonstrated using a compact nuclear simulator.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.249-251
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• 2021

In this paper, we introduce the deep-learning system using Tensorflow for recognizing situations that can occur fall situations when the elderly are moving or standing. Fall detection uses the LSTM (long short-term memory) learned using Tensorflow to determine whether it is a fall or not by data measured from wearable accelerator sensor. Learning is carried out for each of the 7 behavioral patterns consisting of 4 types of activity of daily living (ADL) and 3 types of fall. The learning was conducted using the 3-axis acceleration sensor data. As a result of the test, it was found to be compliant except for the GDSVM(Gravity Differential SVM), and it is expected that better results can be expected if the data is mixed and learned.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.70-75
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• 2023

With the recent proliferation of memory-intensive workloads such as deep learning, analyzing memory access characteristics for efficient memory management is becoming increasingly important. Since read and write operations in memory access have different characteristics, an efficient memory management policy should take into accountthe characteristics of thesetwo operationsseparately. Although some previous studies have considered the different characteristics of reads and writes, they require a modified hardware architecture supporting read bits and write bits. Unlike previous approaches, we propose a software-based management policy under the existing memory architecture for considering read/write characteristics. The proposed policy logically partitions memory space into the read/write area and the write area by making use of reference bits and dirty bits provided in modern paging systems. Simulation experiments with memory access traces show that our approach performs better than the CLOCK algorithm by 23% on average, and the effect is similar to the previous policy with hardware support.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.204-208
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• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1516-1522
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• 2007

We had proposed the FPA(Fixed Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. The algorithm worked not bad in many area, but it lead to some overhead for memory usage and lengthy computation in the multi classes area. We propose an Repetitive FPA algorithm which repetitively partitioning pattern space in the multi classes area. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.311-320
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• 2021

At present, the secondary school teacher employment examination(SSTEE) is the only gateway to become a national and public secondary teacher in Korea, and after the revision from the 2014 academic year, all the questions of the exam have been converted to supply-type test items, requiring more definitive, accurate, and solid answers. Compared to the selection-type test items that measure recognition memory, the supply-type questions, testing recall memory, require constant memorization and retrieval practices to furnish answers; however, there is not enough learning tools available to support the practices. At this juncture, this study invented a mobile app, called ONE PASS, for the SSTEE. By unpacking the functional mechanisms of the brain, the basis of cognitive processing, this ONE PASS app offers a set of tools that feature brain-based learning principles, such as a personalized study planner, motivation measurement scales, mind mapping, brainstorming, and sample questions from previous tests. This study is expected to contribute to the research on the development of learning contents for applications, and at the same time, it hopes to be of some help for candidates in their exam preparation process.

• The Korean Journal of Physiology and Pharmacology
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• v.24 no.2
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• pp.129-135
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• 2020

SHP2 is an unusual protein phosphatase that functions as an activator for several signaling pathways, including the RAS pathway, while most other phosphatases suppress their downstream signaling cascades. The physiological and pathophysiological roles of SHP2 have been extensively studied in the field of cancer research. Mutations in the PTPN11 gene which encodes SHP2 are also highly associated with developmental disorders, such as Noonan syndrome (NS), and cognitive deficits including learning disabilities are common among NS patients. However, the molecular and cellular mechanism by which SHP2 is involved in cognitive functions is not well understood. Recent studies using SHP2 mutant mice or pharmacological inhibitors have shown that SHP2 plays critical role in learning and memory and synaptic plasticity. Here, we review the recent studies demonstrating that SHP2 is involved in synaptic plasticity, and learning and memory, by the regulation of the expression and/or function of glutamate receptors. We suggest that each cell type may have distinct paths connecting the dots between SHP2 and glutamate receptors, and these paths may also change with aging.