• Advances in Traditional Medicine
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• v.7 no.2
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• pp.182-190
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• 2007

Alzheimer's disease is a neurodegenerative disorder associated with a decline in cognitive abilities. Dementia is one of the aged related mental problems and a characteristic symptom of Alzheimer's disease. Nootropic agents like piracetam and cholinesterase inhibitors like $Donepezil^{\circledR}$ are used in situations where there is organic disorder in learning abilities, but the resulting side-effects associated with these agents have limited their utility. Foeniculum (F.) vulgare Linn. is widely used in Indian traditional systems of medicines and also as a house remedy for nervous debility. The present work was undertaken to assess the potential of F. vulgare as a nootropic and anti-cholinesterase agent in mice. Exteroceptive behavioral models such as Elevated plus maze and Passive avoidance paradigm were employed to assess short term and long term memory in mice. To delineate the possible mechanism through which F. vulgare elicits the anti-amnesic effects, its influence on central cholinergic activity was studied by estimating the whole brain acetylcholinesterase activity. Pretreatment of methanolic extract of fruits of F. vulgare Linn. for 8 successive days, ameliorated the amnesic effect of scopolamine (0.4 mg/kg) and aging induced memory deficits in mice. F. vulgare extract significantly decreased transfer latencies of young mice and aged mice, increased step down latency and exhibited significant anti-acetyl cholinesterase effects, when compared to piracetam, scopolamine and control groups of mice. F. vulgare might prove to be a useful memory restorative agent in the treatment of dementia seen in the elderly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.403-413
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• 2009

We compare three predistortion methods to prevent signal distortion and spectral re-growth due to the high PAPR (peak-to-average ratio) of OFDM signal and the non-linearity of high-power amplifiers. The three predistortion methods are pth order inverse, indirect learning architecture and look up table. The pth order inverse and indirect learning architecture methods requires less memory and has a fast convergence because these methods use a polynomial model that has a small number of coefficients. Nevertheless the convergence is fast due to the small number of coefficients and the simple computation that excludes manipulation of complex numbers by separate compensation for the magnitude and phase. The look up table method is easy to implement due to simple computation but has the disadvantage that large memory is required. Computer simulation result reveals that indirect learning architecture shows the best performance though the gain is less than 1 dB at $BER\;=\;10^{-4}$ for 64-QAM. The three predistorters are adaptive to the amplifier aging and environmental changes, and can be selected to the requirements for implementation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.4
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• pp.352-359
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• 1988

A learning control system which should have memory elements can be designed by utilizing the concept of parameter adaptation for unknown control object system parameters and regard it as a hybrid adaptive control system. A parameter adaptive learning control system applicable to a continuous time system has been already reported. Since there have been rapid developments in digital technology, it is possible to extend a continuous time parameter adaptive learning control system concept to a discrete time case. This problem is treated in this paper. Its justfication is proved and a simulation shows that this algorithms is effective.

• Journal of Korean Elementary Science Education
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• v.24 no.4
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• pp.321-328
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• 2005

In this study the effects of science teaming with the level of inquiry requirement in elementary school science experiment instruction were investigated on cognitive domain. We assigned seventy-three students of the fifth grade into two groups according to the levels of inquiry requirement. After each instruction was implemented, the characteristics of the students' tearning science on cognitive domain were compared and analyzed with the levels of them. The higher level (HL) inquiry-required instruction was more effective in increasing and maintaining the memory on the science teaming than the lower level (LL). Especially, in the aspects of the experimental methods and taking cares which the students engage and perform actively rather than do passively, the memory scores of HL group were higher than those of LL. In addition, the memory scores and the degree of maintenance were higher among students who perceived the instruction as easy and interesting. In conclusion, the HL of instruction could stimulate the students to challenge the problems, thereby make them construct meaning actively and improve the amount and degree of maintenance of memory on science teaming.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• International Journal of Contents
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• v.15 no.3
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• Phonetics and Speech Sciences
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• v.8 no.1
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• pp.63-69
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• 2016

The aims of the present study are two-fold: 1) to explore differences on speech perception between younger and older adults according to noise conditions; and 2) to investigate which cognitive domains are correlated with speech perception. Data were acquired from 15 younger adults and 15 older adults. Sentence recognition test was conducted in four noise conditions(i.e., in-quiet, +5 dB SNR, 0 dB SNR, -5 dB SNR). All participants completed auditory and cognitive assessment. Upon controlling for hearing thresholds, the older group revealed significantly poorer performance compared to the younger adults only under the high noise condition at -5 dB SNR. For older group, performance on Seoul Verbal Learning Test(immediate recall) was significantly correlated with speech perception performance, upon controlling for hearing thresholds. In older adults, working memory and verbal short-term memory are the best predictors of speech-in-noise perception. The current study suggests that consideration of cognitive function for older adults in speech perception assessment is necessary due to its adverse effect on speech perception under background noise.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.1
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• pp.1-10
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• 2021

This study proposed a Long-Short Term Memory network to consider changes in emotion over time, and applied an attention mechanism to give weights to the emotion states that appear at specific moments. We used 32 channel EEG data from DEAP database. A 2-level classification (Low and High) experiment and a 3-level classification experiment (Low, Middle, and High) were performed on Valence and Arousal emotion model. As a result, accuracy of the 2-level classification experiment was 90.1% for Valence and 88.1% for Arousal. The accuracy of 3-level classification was 83.5% for Valence and 82.5% for Arousal.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.516-518
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• 2022

In this paper, we introduce a dependence of number of nodes of hidden-layer in fall detection system using Long Short-Term Memory that can detect falls. Its training is carried out using the parameter theta(θ), which indicates the angle formed by the x, y, and z-axis data for the direction of gravity using a 3-axis acceleration sensor. In its learning, validation is performed and divided into training data and test data in a ratio of 8:2, and training is performed by changing the number of nodes in the hidden layer to increase efficiency. When the number of nodes is 128, the best accuracy is shown with Accuracy = 99.82%, Specificity = 99.58%, and Sensitivity = 100%.

• The Mathematical Education
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• v.57 no.2
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• pp.137-155
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• 2018

From the assumption that an individual's working memory capacity is limited, the cognitive load theory is concerned with providing adequate instructional design so as to avoid overloading the learner's working memory. Based on the cognitive load theory, this study aimed to provide implications for effective problem-based collaborative teaching and learning design by analyzing the level of middle school students' cognitive load which is perceived according to the problem types(short answer type, narrative type, project) in the process of collaborative problem solving in middle school function part. To do this, this study analyzed whether there is a relevant difference in the level of cognitive load for the problem type according to the math achievement level and gender in the process of cooperative problem solving. As a result, there was a relevant difference in the task burden and task difficulty perceived according to the types of problems in both first and second graders in middle schools students. and there was no significant difference in the cognitive effort. In addition, the efficacy of task performance differed between first and second graders. The significance of this study is as follows: in the process of collaborative problem solving learning, which is most frequently used in school classrooms, it examined students' cognitive load according to problem types in various aspects of grade, achievement level, and gender.