• ETRI Journal
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• v.44 no.4
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• pp.672-685
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• 2022

This article proposes a subject-independent application of brain-computer interfacing (BCI). A 32-channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come-here) across 13 subjects. A deep long short-term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain. The results show a maximum accuracy of 73.56% and a network prediction time (NPT) of 0.14 s which are superior to other state-of-the-art techniques in the literature. Our analysis reveals that the alpha band can recognize SI better than other EEG frequencies. To reinforce our findings, the above work has been compared by models based on the gated recurrent unit (GRU), convolutional neural network (CNN), and six conventional classifiers. The results show that the LSTM model has 46.86% more average accuracy in the alpha band and 74.54% less average NPT than CNN. The maximum accuracy of GRU was 8.34% less than the LSTM network. Deep networks performed better than traditional classifiers.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.582-588
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• 2018

Accident diagnosis is one of the complex tasks for nuclear power plant (NPP) operators. In abnormal or emergency situations, the diagnostic activity of the NPP states is burdensome though necessary. Numerous computer-based methods and operator support systems have been suggested to address this problem. Among them, the recurrent neural network (RNN) has performed well at analyzing time series data. This study proposes an algorithm for accident diagnosis using long short-term memory (LSTM), which is a kind of RNN, which improves the limitation for time reflection. The algorithm consists of preprocessing, the LSTM network, and postprocessing. In the LSTM-based algorithm, preprocessed input variables are calculated to output the accident diagnosis results. The outputs are also postprocessed using softmax to determine the ranking of accident diagnosis results with probabilities. This algorithm was trained using a compact nuclear simulator for several accidents: a loss of coolant accident, a steam generator tube rupture, and a main steam line break. The trained algorithm was also tested to demonstrate the feasibility of diagnosing NPP accidents.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.239-241
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• 1999

This paper proposes a novel wavelet transform and Kohonen neural network based technique for short-time load forecasting of power systems. Firstly. Kohonen Self-organizing map(KSOM) is applied to classify the loads and then the Daubechies D2, D4 and D10 wavelet transforms are adopted in order to forecast the short-term loads. The wavelet coefficients associated with certain frequency and time localisation are adjusted using the conventional multiple regression method and then reconstructed in order to forecast the final loads through a four-scale synthesis technique. The outcome of the study clearly indicates that the proposed composite model of Kohonen neural network and wavelet transform approach can be used as an attractive and effective means for short-term load forecasting.

• Convergence Security Journal
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• v.17 no.5
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• pp.57-61
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• 2017

The National research networks exist to support advanced science and technology in each country. The national research network must meet the requirements of science and technology in each field and continuously develop around the continuously changing environment. In recent years, demand for data - oriented science and technology research forms has been increasing. To cope with this demand, advanced national research networks are establishing mid - to long - term strategies. In this paper, the status of the advanced research network and trends and requirements of the national research network are analyzed from the viewpoint of the change of the research network environment, the change of science / technology, and the change of industry and life. In order to respond to the analyzed requirements, we propose mid - to long - term development directions and plans for establishing future network backbone, providing user - centered collaborative research environment, providing global collaborative network service, and providing high - tech science and technology research data information protection service.

• The Korean Journal of Health Service Management
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• v.2 no.1
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• pp.125-136
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• 2008

Government came to enforce long-term care insurance system in preparation for the rapid aging society. Whether this system is successful or not depends on the professionalism of long-term care-givers who are professional population in charge of care service. Currently in the early stage of enforcement, such problems as a race cutting fee resulted from numerical increase of educational facilities, insolvent operation, degradation of education level resulted from unprofessional instructor, are pointed out. As a mean of manpower-training on long-term care insurance system, this study is to research public-private-university network model of the Academy of Continuing Education attached to Ko Sin University which is the case of Yong Do Gu Busan city. Networking between the vision and development strategy of Yong Do Gu on continuing education city, education system on community manpower-training supported by Ko Sin University, and service field of welfare for the elderly can not only contribute to the professionalism of long-term care-givers but also play an ideal role in manpower-utilization and arrangement of community. Through this networking, high quality of education level and circumstance, using the existing infra, manpower-training and utilization for continuing education of Yong Do Gu can be accomplished. Additionally, the connection with facilities related with welfare for the elderly can contribute to professionalism and accountability of manpower-networking.

• Journal of Korean Society on Water Environment
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• v.29 no.3
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• pp.343-353
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• 2013

Flow duration curve (FDC) can be developed by linking the daily flow data of stream flow monitoring network to 8-day interval flow data of the unit watersheds for the management of Total Maximum Daily Loads. This study investigated the applicable method for the development of long term FDC with the selection of the stream flow reference sites, and suggested the development of the FDC in 4 river basins. Out of 142 unit watersheds in 4 river basins, 107 unit watersheds were shown to estimate daily flow data for the unit watersheds from 2006 to 2010. Short term FDC could be developed in 64 unit watersheds (45%) and long term FDC in 43 unit watersheds (30%), while other 35 unit watersheds (25%) were revealed to have difficulties in the development of FDC itself. Limits in the development of the long term FDC includes no stream monitoring sites in certain unit watersheds, short duration of stream flow data set and missing data by abnormal water level measurements on the stream flow monitoring sites. To improve these limits, it is necessary to install new monitoring sites in the required areas, to keep up continuous monitoring and make normal water level observations on the stream flow monitoring sites, and to build up a special management system to enhance data reliability. The development of long term FDC for the unit watersheds can be established appropriately with the normal and durable measurement on the selected reference sites in the stream flow monitoring network.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1660-1671
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• 2004

This paper proposes a method for measuring document similarity between two documents. One of the most significant ideas of the method is to estimate the degree of similarity between two documents based on the frequencies of terms and term-pair, existing in both the two documents. In contrast to conventional methods which takes only one feature into account, the proposed method considers several features at the same time and meatures the similarity using a neural network. To prove the superiority of our method, two experiments have been conducted. One is to verify whether the two input documents are from the same document or not. The other is a problem of information retrieval with a document as the query against a large number of documents. In both the two experiments, the proposed method shows higher accuracy than two conventional methods, Cosine similarity measurement and a term-pair method.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.469-476
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• 2022

This study developed a Recurrent Neural Network model implemented through Long Short-Term Memory (LSTM) that generates long-term tidal level data at Busan Port using tide observation data. The tide levels in Busan Port were predicted by the Korea Hydrographic and Oceanographic Administration (KHOA) using the tide data observed at Busan New Port and Tongyeong as model input data. The model was trained for one month in January 2019, and subsequently, the accuracy was calculated for one year from February 2019 to January 2020. The constructed model showed the highest performance with a correlation coefficient of 0.997 and a root mean squared error of 2.69 cm when the tide time series of Busan New Port and Tongyeong were inputted together. The study's finding reveal that long-term tidal level data prediction of an arbitrary port is possible using the deep learning recurrent neural network model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.522-541
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• 2016

Network virtualization provides an effective way to overcome the Internet ossification problem. As one of the main challenges in network virtualization, virtual network embedding refers to mapping multiple virtual networks onto a shared substrate network. However, existing heuristic embedding algorithms evaluate the embedding potential of the nodes simply by the product of different resource attributes, which would result in an unbalanced embedding. Furthermore, ignoring the hops of substrate paths that the virtual links would be mapped onto may restrict the ability of the substrate network to accept additional virtual network requests, and lead to low utilization rate of resource. In this paper, we introduce and extend five node attributes that quantify the embedding potential of the nodes from both the local and global views, and adopt the technique for order preference by similarity ideal solution (TOPSIS) to rank the nodes, aiming at balancing different node attributes to increase the utilization rate of resource. Moreover, we propose a novel two-stage virtual network embedding algorithm, which maps the virtual nodes onto the substrate nodes according to the node ranks, and adopts a shortest path-based algorithm to map the virtual links. Simulation results show that the new algorithm significantly increases the long-term average revenue, the long-term revenue to cost ratio and the acceptance ratio.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2155-2158
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• 2003

A term of home automation that was in fashion only a few years ago has not been used any more. Nowadays, We have been used a term of home network or digital home than home automation much. While internet infra is diffused at home, data network corp., communication corp., electric appliance corp. and home automation control system corp. which we did not mind each other particularly constructed consortium, and they have designs on home network market. Also, cellular phone's growth tried home networking by using not only wired internet but also broadband wireless communication. Regardless, many solutions are coming out it is few to be applied to real life because the standard is not determined with the protocol each other. Therefore, we developed home network system using USB(Universal Serial Bus) that has the possibility most in home networking standard. The mobile USB home control system is excellent at expansibility and portability. Also we can complete low cost and stable system using an embedded system.