• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.268-273
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• 2017

Recently, deep learning encoder-based approach has been actively applied in the field of sentiment classification. However, Long Short-Term Memory network deep learning encoder, the commonly used architecture, lacks the quality of vector representation when the length of the documents is prolonged. In this study, for effective classification of the sentiment documents, we suggest the use of attention method-based deep learning encoder that generates document vector representation by weighted sum of the outputs of Long Short-Term Memory network based on importance. In addition, we propose methods to modify the attention method-based deep learning encoder to suit the sentiment classification field, which consist of a part that is to applied to window attention method and an attention weight adjustment part. In the window attention method part, the weights are obtained in the window units to effectively recognize feeling features that consist of more than one word. In the attention weight adjustment part, the learned weights are smoothened. Experimental results revealed that the performance of the proposed method outperformed Long Short-Term Memory network encoder, showing 89.67% in accuracy criteria.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.155-161
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• 2023

97.5% of our country's exports and 87.2% of imports are transported by sea, making ports an important component of the Korean economy. To efficiently operate these ports, it is necessary to improve the short-term prediction of port water volume through scientific research methods. Previous research has mainly focused on long-term prediction for large-scale infrastructure investment and has largely concentrated on container port water volume. In this study, short-term predictions for petroleum and liquefied gas cargo water volume were performed for Ulsan Port, one of the representative petroleum ports in Korea, and the prediction performance was confirmed using the deep learning model LSTM (Long Short Term Memory). The results of this study are expected to provide evidence for improving the efficiency of port operations by increasing the accuracy of demand predictions for petroleum and liquefied gas cargo water volume. Additionally, the possibility of using LSTM for predicting not only container port water volume but also petroleum and liquefied gas cargo water volume was confirmed, and it is expected to be applicable to future generalized studies through further research.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.95-102
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• 2019

This study was to investigate the effect of eye movement and intentional finger movement on cognitive ability. Normal adult subjects were randomly divided into two groups: saccadic eye movement(SEM) and intentional finger movement(IFM). After 2 weeks of intervention, Digit span was used for short-term memory test and N-back was used for working memory test. As a result, the short-term memory of the IFM group increased significantly over time, and the follow-up test showed difference between group. The IFM group's the execution time, the error count and the accuracy rate of n-back item showed significant effects over time. The SEM group's the execution time and the accuracy of n-back item showed significant effects over time. In conclusion, the IFM method, which is a multiple stimulus that can activate the cerebral cortex more extensively than the single stimulus SEM, may be more useful as an intervention method of cognitive function improvement.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.1
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• pp.83-96
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• 2024

Purpose : This study was conducted to determine the effects of a brain spinning program on cognitive function, body composition, health related fitness and physical self-efficacy of children and adolescents. Methods : This study, 34 children and adolescents were selected and divided into two groups : the exercise group (n=16), which received a brain spinning program and the control group (n=16), which did not receive any exercise program. The program was conducted for 30 minutes three times a week for 4 weeks, and the cognitive function, body composition, health related fitness and physical self-efficacy were measured both before and after the program. Results : The exercise group, which received a brain spinning program showed a significant increase in short-term memory (p<.05) and working memory (p<.01), and muscle mass increased significantly only in the exercise group (p<.05). In addition, left grip strength increased in the exercise group (p<.01), and the maximum oxygen intake decreased significantly only in the control group (p<.05), and Sit-forward bend increased significantly only in the exercise group (p<.01). Physical self-efficacy significantly increased only in the exercise group (p<.05). Conclusion : In summary, short-term memory, cognitive efficiency, working memory, muscle mass, left grip strength, maximum oxygen intake, and left forward bending in children and adolescents significantly increased after the 4-week brain spinning program. However, the control group that was not provided with the 4-week brain spinning program showed a significant increase in body weight and a significant decrease in maximum oxygen intake. In conclusion, the 4-week brain spinning program has positive effects on short-term memory, cognitive function, muscle mass, muscle strength, cardiorespiratory endurance, flexibility, and physical self-efficacy.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.393-398
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• 2007

In this paper, we propose the development of Artificial Hippocampus Algorithm(AHA) which remodels a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 4 steps system (EC, DG CA3, and CA1) and improve speed of teaming by addition of modulator to long-term memory teaming. In hippocampus system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labeled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CA1 region, convergence of connection weight which is used long-term memory is learned fast a by neural network which is applied modulator. To measure performance of Artificial Hippocampus Algorithm, PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) are applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by AHA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.297-304
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• 2013

This research explores the effect of TV news camerawork and viewers' involvement on memory of news through experiment. For this experimental research, 2 groups of subjects composed of university students were exposed to different types of TV news and responded to survey questions which were analysed by SPSS program. This research found that camerawork of TV news doesn't have an effect on short-term memory but on long-term memory. Though the fact viewers' involvement has a positive effect on shot-term and long-term memory was found, interactive effect of viewers' involvement and camerawork as an peripheral clue was not found.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1232-1245
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• 2021

In this study, prediction of product sales as they relate to changes in temperature is proposed. This model uses long short-term memory (LSTM), which has shown excellent performance for time series predictions. For verification of the proposed sales prediction model, the sales of short pants, flip-flop sandals, and winter outerwear are predicted based on changes in temperature and time series sales data for clothing products collected from 2015 to 2019 (a total of 1,865 days). The sales predictions using the proposed model show increases in the sale of shorts and flip-flops as the temperature rises (a pattern similar to actual sales), while the sale of winter outerwear increases as the temperature decreases.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• The Journal of Bigdata
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• v.5 no.1
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• pp.1-9
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• 2020

The study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and in based on artificial intelligence-based HPAI spread analysis and patterning. The model that is actively used in time series and text mining recently is LSTM (Long Short-Term Memory Models) model utilizing deep learning model structure. The LSTM model is a model that emerged to resolve the Long-Term Dependency Problem that occurs during the Backpropagation Through Time (BPTT) process of RNN. LSTM models have resolved the problem of forecasting very well using variable sequence data, and are still widely used.In this paper study, we used the data of the Call Detailed Record (CDR) provided by KT to identify the migration path of people who are expected to be closely related to the virus. Introduce the results of predicting the path of movement by learning the LSTM model using the path of the person concerned. The results of this study could be used to predict the route of HPAI propagation and to select routes or areas to focus on quarantine and to reduce HPAI spread.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.79-85
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• 2019

In this paper, we propose a short-term power forecasting method by applying Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network to Internet of Things (IoT) power meter. We analyze performance based on real power consumption data of households. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean percentage error (MPE), mean squared error (MSE), and root mean squared error (RMSE) are used as performance evaluation indexes. The experimental results show that the GRU-based model improves the performance by 4.52% in the MAPE and 5.59% in the MPE compared to the LSTM-based model.