• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.70-73
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• 1992

This paper describes the modeling of human memory using a nerve field model which is proposed for modeling the mechanism of brain mathematically. In our model, two phases of memory, retention and recollection, are focused on. The former consists of two stages, short-term memory (STM) and long-term memory (LTM). The proposed model consists of three parts, the STM Layer, LTM Layer and the Intermediate Layer between them. Each of these is constructed by a nerve field. In the STM Layer, memorized information is retained dynamically in the form of the reverberating states of units within the layer, while in the LTM Layer, it is stored statically in the form of structures of the weight on the links between units. the Intermediate Layer is introduced to translate this dynamic representation in the STM Layer to the LTNI Layer, and also to extract the static information from the STM Layer. In addition to this, we consider the recollection of information stored in the LTM. Finally, the behavior of this model is demonstrated by computer simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5580-5593
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• 2019

A malicious Uniform Resource Locator (URL) recognition and detection method based on the combination of Attention mechanism with Convolutional Neural Network and Long Short-Term Memory Network (Attention-Based CNN-LSTM), is proposed. Firstly, the WHOIS check method is used to extract and filter features, including the URL texture information, the URL string statistical information of attributes and the WHOIS information, and the features are subsequently encoded and pre-processed followed by inputting them to the constructed Convolutional Neural Network (CNN) convolution layer to extract local features. Secondly, in accordance with the weights from the Attention mechanism, the generated local features are input into the Long-Short Term Memory (LSTM) model, and subsequently pooled to calculate the global features of the URLs. Finally, the URLs are detected and classified by the SoftMax function using global features. The results demonstrate that compared with the existing methods, the Attention-based CNN-LSTM mechanism has higher accuracy for malicious URL detection.

• Wind and Structures
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• v.38 no.1
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• Korean Journal of Cognitive Science
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• v.11 no.3_4
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• pp.33-43
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• 2000

The distinction between short-term store (STS) and long-term store (LTS) has been made in the perspective of information processing. Memory system theorists have argued that memory could be conceived as multiple memory systems beyond the concept of a single LTS. Popular memory system models are Schacter & Tulving (994)'s multiple memory systems and Squire (987)'s the taxonomy of long-term memory. Those m models agree that amnesic patients have intact STS but impaired LTS and have preserved implicit memory. However. there is a debate about the nature of the long-term memory impairment. One model considers amnesic deficit as a selective episodic memory impairment. whereas the other sees the deficits as both episodic and semantic memory impairment. At present, it remains unclear that episodic memory should be distinguished from semantic memory in terms of retrieval operation. The distinction between declarative memory and nondeclarative memory would be the alternative way to reflect explicit memory and implicit memory. The research focused on the function of frontal lobe might give clues to the debate about the nature of LTS.

• 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.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.4
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• pp.878-890
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• 2013

The purpose of this study is to apply the PEOE class model that can enhance students' scientific creative problem-solving ability and self-directed learning ability in the middle school science subject and analyze the effects of it on students' long- and short-term retention, scientific creative problem-solving ability, and self-directed learning characteristics. And the paper has gained the following results: First, according to the result of analysis through the pre-test, post-test, and delay test to examine the effects of PEOE-based class on learners' long- and short-term retention, it is found to be statistically significant in the significant level of .05. In other words, the class using PEOE influences learners' short-term retention significantly, but it is even more effective in transmitting the concept that students acquire into their long-term memory. Second, according to the result of analysis through the pre-test and post-test to examine the effects of PEOE-based class on learners' scientific creative problem-solving ability, it is found to be statistically significant in the significant level of .05 in general. However, 'elaboration' and 'originality', the subfactors of scientific creative problem-solving ability, do not indicate significant effects. Third, according to the result of analysis through the pre-test and post-test to examine the effects of PEOE-based class on learners' self-directed learning characteristics, it is found to be statistically significant in the significant level of .05 as a whole. However, 'openness' and 'future-oriented self-understanding', the subfactors of self-directed learning characteristics, do not exert significant effects. Based on the above study results, it can be concluded that PEOE-based class is more effective for learners' academic achievement in science, scientific creative problem-solving ability, and self-directed learning characteristics than lecture-method instruction regarding the middle school science unit of 'The Properties of Air and Weather Change'.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.561-569
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• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1577-1589
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• 2023

Recently, the necessity of predicting changes for monitoring ocean is widely recognized. In this study, we performed a time series prediction of remote-sensing reflectance (Rrs), which can indicate changes in the ocean, using Geostationary Ocean Color Imager (GOCI) data. Using GOCI-I data, we trained a multi-scale Convolutional Long-Short-Term-Memory (ConvLSTM) which is proposed in this study. Validation was conducted using GOCI-II data acquired at different periods from GOCI-I. We compared model performance with the existing ConvLSTM models. The results showed that the proposed model, which considers both spatial and temporal features, outperformed other models in predicting temporal trends of Rrs. We checked the temporal trends of Rrs learned by the model through long-term prediction results. Consequently, we anticipate that it would be available in periodic change detection.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.4
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• pp.169-177
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• 2009

In come cases, people differently respond on the same joke or thoughtless behavior - sometimes like it and laugh, another time feel annoyed or angry. This fact is explained that experiences which we had in the past are remembered by emotional memory, so they cause different responses. When people face similar situation or feel similar emotion, they evoke the emotion experienced in the past and the emotional memory affects current emotion. This paper suggested the mechanism of the emotional memory using SOM through the similarity between the emotional memory and SOM learning algorithm. It was assumed that the mechanism of the emotional memory has also the characteristics of association memory, long-term memory and short-term memory in its process of remembering emotional experience, which are known as the characteristics of the process of remembering factual experience. And then these characteristics were applied. The mechanism of the emotional memory designed like this was applied to toy hammer game and I measured the change in the power of toy hammer caused by differently responding on the same stimulus. The mechanism of the emotional memory suggest in above is expected to apply to the fields of game, robot engineering, because the mechanism can express various emotions on the same stimulus.

• Therapeutic Science for Rehabilitation
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• v.7 no.3
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• pp.47-58
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• 2018

Objective : This study was conducted to investigate the effects of iPad applications on improvement of attention and short-term memory in mild dementia. Methods : A single-case experimental study using A-B-A design was conducted. A total of 20 sessions, including 5 each for baseline phase A and A' and 10 for the intervention phase, were provided to the subject. Interventions were only provided during the intervention phase and were iOS-based iPad applications named "Memorado-Moving Balls" and "Circles." "Fit Brains-Matching Pairs" and "Fit-Brains-Spot the Difference" were used for each session to evaluate attention and short-term memory. MMSE-K, K-TMT-e A and B, and DST assessment tools were used pre- and post-intervention to assess attention and memory. Result : Fit Brains scores indicated improvement in both attention and memory during the intervention phase. K-TMT-e A showed 3 increased correct points and 3 reduced error points, and B showed 7 increased correct points and 2 reduced error points in post-tests, but the DST and MMSE-K showed no meaningful change. Conclusion : This single-case study identified improvements in attention and short-term memory in a person with mild dementia using iPad applications. Further studies regarding different applications and larger samples with long-term designs are necessary.