• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1285-1289
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• 2004

Elman Discrete Recurrent Neural Networks Model(EDRNNM) was used to be a suitable short-term hydrological forecasting tool yielding a very high degree of flood stage forecasting accuracy at Musung station of Wi-stream one of IHP representative basins in South Korea. A relative new approach method has recurrent feedback nodes and virtual small memory in the structure. EDRNNM was trained by using two algorithms, namely, LMBP and RBP The model parameters, optimal connection weights and biases, were estimated during training procedure. They were applied to evaluate model validation. Sensitivity analysis test was also performed to account for the uncertainty of input nodes information. The sensitivity analysis approach could suggest a reduction of one from five initially chosen input nodes. Because the uncertainty of input nodes information always result in uncertainty in model results, it can help to reduce the uncertainty of EDRNNM application and management in small catchment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.63
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• pp.13-21
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• 2001

The present study compared the two experimental methods on inquiring the image structure of odors: Presenting a stimulus is one, and not presenting a stimulus is the other. For experiment one, five odors were presented, and the subjects were instructed to evaluate the odors on 7-point scale for each of the 25 adjectives. For experiment two, no odor was presented, and the subjects were instructed to perform the pair-wise comparisons for the each pair of two adjectives on their similarities on 7-point scale. The data from the two experiments were analysed and compared using MDS(Multi-Dimensional Scaling), Correlation, Cluster Analysis. The results showed that there was no structural differences between two experimental methods in term of the Image structure of odors. But, minor disparity was found between two methods in terms of density of distribution of the adjectives. It was construed that the difference came from the difference of the memory that was used for each of the experiments; that is, short term memory for experiment one and long-term memory for experiment two.

• Korean Journal of Cognitive Science
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• v.32 no.3
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• pp.117-139
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• 2021

The way of change detection in which presence of a different item is determined between memory and test arrays with a brief in-between time interval resembles how visual search is done considering that the different item is searched upon the onset of a test array being compared against the items in memory. According to the resemblance, the present study examined whether varying the probability of change occurrence in a visual short-term memory-based change detection task can influence the aspect of response-decision making (i.e., change prevalence effect). The simple-feature change detection task in the study consisted of a set of four colored boxes followed by another set of four colored boxes between which the participants determined presence or absence of a color change from one box to the other. The change prevalence was varied to 20, 50, or 80% in terms of change occurrences in total trials, and their change detection errors, detection sensitivity, and their subsequent RTs were analyzed. The analyses revealed that as the change prevalence increased, false alarms became more frequent while misses became less frequent, along with delayed correct-rejection responses. The observed change prevalence effect looks very similar to the target prevalence effect varying according to probability of target occurrence in visual search tasks, indicating that the background principles deriving these two effects may resemble each other.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.239-260
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• 2023

The underwater acoustic wireless communication networks are generally formed by the different autonomous underwater acoustic vehicles, and transceivers interconnected to the bottom of the ocean with battery deployed modems. Orthogonal frequency division multiplexing (OFDM) has become the most popular modulation technique in underwater acoustic communication due to its high data transmission and robustness over other symmetrical modulation techniques. To maintain the operability of underwater acoustic communication networks, the power consumption of battery-operated transceivers becomes a vital necessity to be minimized. The OFDM technology has a major lack of peak to average power ratio (PAPR) which results in the consumption of more power, creating non-linear distortion and increasing the bit error rate (BER). To overcome this situation, we have contributed our symmetry research into three dimensions. Firstly, we propose a machine learning-based underwater acoustic communication system through long short-term memory neural network (LSTM-NN). Secondly, the proposed LSTM-NN reduces the PAPR and makes the system reliable and efficient, which turns into a better performance of BER. Finally, the simulation and water tank experimental data results are executed which proves that the LSTM-NN is the best solution for mitigating the PAPR with non-linear distortion and complexity in the overall communication system.

• Korean Journal of Biological Psychiatry
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• v.8 no.1
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• pp.131-139
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• 2001

This study was conducted to evaluate the effect of PTSD on memory function and hippocampal volume, and to identify major variables correlated to hippocampal volume and memory function. Thirty four Vietnam veterans were collected for this study, among whom eighteen were PTSD patients and sixteen were combat control subjects. The author used Impact of Event Scale(IES), Combat Exposure Scale(CES), Hamilton Depression Rating Scale(HDRS) and Beck Depression Inventory (BDI). Korea Memory Assessment Scale(K-MAS) was assessed for memory function. Magnetic resonance imaging(MRI) was used to measure hippocampal volume. There were significant differences between PTSD and Non-PTSD veterans in IES, HDRS and BDI. Significant difference was found in verbal memory and total memory of K-MAS between PTSD and Non-PTSD veterans. There was significant difference in hippocampal volume between PTSD and Non-PTSD veterans. Short term memory, verbal memory and total memory were positively correlated to hippocampal volume. Hippocampal volume was negatively correlated to IES, HDRS, and BDI. These results suggest that PTSD severity be associated with hippocampal atrophy and memory dysfunction. Reduced or smaller hippocampal volume may be preexisting risk factor for stress exposure or the development of PTSD on combat exposure.

• Journal of the Korean earth science society
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• v.26 no.6
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• pp.469-476
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• 2005

The objective of this study is to investigate the effects of a new learning method called, 'e-Learning,' by applying this method on a middle school science curriculum and study the influence it has on the students’ short and long term memory. The study was performed on two classes of sixth grade students at 'K middle school' in Yangsan. By handing out structured study assignment in e-Learning, I was able to observe how it affected the learners’ short and long term retention. The results of the study were as follows: First, classes that underwent studies using e-Learning did not show any influence on short term retention. Second, e-Learning had positive influence on long term retention. Third, learners who experienced e-Learning had positive cognition on e-Learning.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.322-323
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• 2022

The most necessary thing for the optimal operation of a water purification plant is to accurately predict the pattern and amount of tap water used by consumers. The required amount of tap water should be delivered to the drain using a pump and stored, and the required flow rate should be supplied in a timely manner using the minimum amount of electrical energy. The short-term demand forecasting required from the point of view of energy optimization operation among water purification plant volume predictions has been made in consideration of seasons, major periods, and regional characteristics using time series analysis, regression analysis, and neural network algorithms. In this paper, we analyzed energy management methods through AI-based complex sensor applicability analysis such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units), which are types of cyclic neural networks.

• Korean Institute of Interior Design Journal
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• v.24 no.2
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• pp.171-179
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• 2015

The aim of this study is to figure out the preference features on design components of shop facade on the basis of the questionnaire survey on short-term memory and sensory memory of human right after an image experiment. As for a preceding research, this study examined the design features of facade into tangible elements and intangible elements, and also classified them into physical, aesthetical, marketing and symbolic components in detail. And, it extracted 5 representative elements in preceding studies including shape, material, pattern, color and sign, which is the standard of a questionnaire survey and preference analysis. The subjects of the experiment were 30 men and women who were over 20 years old majoring interior design. They were exposed to 20 images with 10 seconds respectively through a video, and were asked to respond the questionnaire promptly. The findings of preference analysis of design components of facade including shape, material, pattern, color and sign are as follows. Firstly, shape was the most interesting and attracting component, and designs applied with shape of objects such as 'web', 'drawer', 'wheel' and 'button' obtained high preference. Secondly, as for material, block, steel, exposed concrete board attracted higher preference as memorable materials than other materials. Material was affected by shape, pattern and color. Thirdly, pattern was the most lasting element. Designed pattern had higher preference than simple pattern. Fourthly, as for color, red and green with strong stimulation and attention attained priority having long lasting memory. Fifthly, when visiting a shop, sign out of 5 elements of shape, material, pattern, color and sign drew attention the most. As for the preference of location of sign, 'center top' was the most noticeable. The findings of this study could be utilized for facade design, and also could be used for commercialization considering highly preferred components, and top preference aspects of such elements. advised that to give an impression to customers is important to make a successful design for sales marketing, which, in turn, would lead customers to revisit the shop.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.