• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.308-320
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• 2019

This study aims at investigating the major symptoms that help family carers detect mental illness in elderly patients. Another purpose of this study is to empirically verify the major factors determining the utilization of mental health services with a focus on family carers. The results of this study are as follows. First, the most commonly detected symptoms that caused the family carers to suspect mental illness in the elderly patients were memory decline and other forms of cognitive function decline. Second, the determinants of the elderly's utilization of mental health services included the patient's long-term care insurance level, the age of the family carer, the period of care, the level stress associated with the provision of care felt by the carer, his understanding of geriatric mental illness, and the level of perception about community mental health services. Based on these findings, this study suggests policies and practical implications for the early detection of and response to elderly mental health problems and the utilization of related services from the viewpoint of the family carers of the elderly.

• The Korean Journal of Archival Studies
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• no.51
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• pp.45-87
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• 2017

In recent years, there were some who have conducted research on deriving the social and historical meanings of records through the analysis of specific records collections. This trend is an attempt to pay attention to human actions in the society and to read the society again through the records created by such actions. In this study, I derive various meanings and values of these records through the analysis of the "Laos Renewable Energy Support Activities" collection. Moreover, I study how the collection was reconstructed by the Human and Memory Archives. The "Laos Renewable Energy Support Activities" is the personal record of the donor who led the project, and contains the process and results of the project. Through this collection, I was able to look at the life of the donor as a foreign aid activist in Laos and realized his values. Furthermore, through the business process record, I was able to discover the implications of climate change response overseas aid projects. In addition, I was able to look at the culture and environment of Laos through the eyes of the donor who has been residing there for a long time.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.733-740
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• 2021

The purpose of this study is to present the direction of elementary school AI education by analyzing cases of classes related to AI education in actual school settings. For this purpose, 19 classes were collected as elementary school class cases based on AI education. According to the result of analyzing the class case, it was confirmed that the class was designed in a hybrid aspect of learning content and method using AI. As a result of analyzing the achievement standards and learning goals, action verbs related to memory, understanding, and application were found in 8 classes using AI from a tool perspective. When class was divided into introduction, development, and rearrangement stages, the AI education element appeared the most in the development stage. On the other hand, when looking at the ratio of learning content and learning method of AI education elements in the development stage, the learning time for approaching AI education as a learning method was overwhelmingly high. Based on this, the following implications were derived. First, when designing the curriculum for schools and grades, it should be designed to comprehensively deal with AI as a learning content and method. Second, to supplement the understanding of AI, in the short term, it is necessary to secure the number of hours in practical subjects or creative experience activities, and in the long term, it is necessary to secure information subjects.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.71-80
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• 2021

The selection of an appropriate neural network algorithm is an important step for accurate data prediction in machine learning. Many algorithms based on basic artificial neural networks have been devised to efficiently predict future data. These networks include deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) neural networks. Developers face difficulties when choosing among these networks because sufficient information on their performance is unavailable. To alleviate this difficulty, we evaluated the performance of each algorithm by comparing their errors and processing times. Each neural network model was trained using a tax dataset, and the trained model was used for data prediction to compare accuracies among the various algorithms. Furthermore, the effects of activation functions and various optimizers on the performance of the models were analyzed The experimental results show that the GRU and LSTM algorithms yields the lowest prediction error with an average RMSE of 0.12 and an average R2 score of 0.78 and 0.75 respectively, and the basic DNN model achieves the lowest processing time but highest average RMSE of 0.163. Furthermore, the Adam optimizer yields the best performance (with DNN, GRU, and LSTM) in terms of error and the worst performance in terms of processing time. The findings of this study are thus expected to be useful for scientists and developers.

• Food Science and Industry
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• v.55 no.2
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• pp.154-165
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• 2022

Humans have been breeding the mulberry silkworm for the long period of time to obtain silk fabric and nutrient-rich pupae. Currently, silkworm larvae, pupae, and silk-Fibroin hydrolysates are registered as food raw materials, while silkworm feces and Bombyx batryticatus are registered as Korean traditional medicines. Among sericulture products, individually recognized health functional food ingredients include silk-protein acid-hydrolysates for immunity enhancement, Fibroin-hydrolysates for memory improvement, and freeze-dried 5^th instar and 3^rd-day-silkworm powder for lowering-blood sugar. Recently, HongJam produced by steaming and freeze-drying mature silkworms were reported to have various health-promoting effects such as preventing the onset of Alzheimer's disease and Parkinson's disease, enhancing gastro-intestinal functions, improving skin-whitening and hair growth, and extending healthspan. By consuming silkworm products with various health-promoting effects, it is possible to increase the healthspan of human beings, thereby reducing personal and national medical expenses, resulting in increasing the individual's happiness.

• Journal of Industrial Convergence
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• v.20 no.4
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• pp.23-30
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• 2022

Recently, with the development of artificial intelligence (AI) and deep learning, the importance of conversational artificial intelligence chatbots is being highlighted. In addition, chatbot research is being conducted in various fields. To build a chatbot, it is developed using an open source platform or a commercial platform for ease of development. These chatbot platforms mainly use RNN and application algorithms. The RNN algorithm has the advantages of fast learning speed, ease of monitoring and verification, and good inference performance. In this paper, a method for improving the inference performance of RNNs and applied algorithms was studied. The proposed method used the word group expansion learning technique of key words for each sentence when RNN and applied algorithm were applied. As a result of this study, the RNN, GRU, and LSTM three algorithms with a cyclic structure achieved a minimum of 0.37% and a maximum of 1.25% inference performance improvement. The research results obtained through this study can accelerate the adoption of artificial intelligence chatbots in related industries. In addition, it can contribute to utilizing various RNN application algorithms. In future research, it will be necessary to study the effect of various activation functions on the performance improvement of artificial neural network algorithms.

• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.141-151
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• 2021

This study analyzed the meaning and functions of '-(으)ㄹ 것 같다' and '-나 보다' among the various semantic functions depending on the situation, and discussed ways to train speculative expressions more efficiently by expanding them from traditional teaching methods through visualizations applied visual thinking at real Korean language education. The speculative representation, which is the subject of this study, represents the speaker's speculation about something or situation, with slight differences in meaning depending on the basis of the speculation and the subject of the speculation. We propose a training method that can enhance the diversification and efficiency of teaching-learning through visualization of information or knowledge, speculative representations that exhibit fine semantic differences in various situations. Utilizing visual thinking in language education can simplify and provide language information through visualization of language knowledge, and learners can be efficient at organizing and organizing language knowledge. It also has the advantage of long-term memory of language information through visualization of language knowledge. Attempts of various educational methods that can be applied at the Korean language education site can contribute to establishing a more systematic and efficient education method, which is meaningful in that the visual thinking proposed in this study can give interest and efficiency to international students.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.