• ETRI Journal
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• v.32 no.2
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• pp.184-194
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• 2010

Ethernet ring protection (ERP) technology, which is defined in ITU-T Recommendation G.8032, has been developed to provide carrier grade recovery for Ethernet ring networks. However, the filtering database (FDB) flush method adopted in the current ERP standard has the drawback of introducing a large amount of transient traffic overshoot caused by flooded Ethernet frames right after protection switching. This traffic overshooting is especially critical when a ring provides services to a large number of clients. According to our experimental results, the traditional FDB flush requires a link capacity about sixteen times greater than the steady state traffic bandwidth. This paper introduces four flush optimization schemes to resolve this issue and investigates how the proposed schemes deal with the transient traffic overshoot on a multi-ring network under failure conditions. With a network simulator, we evaluate the performance of the proposed schemes and compare them to the conventional FDB flush scheme. Among the proposed methods, the extended FDB advertisement method shows the fastest and most stable protection switching performance.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1172-1180
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• 2004

Recently, the gene expression data, product of high-throughput technology, appeared in earnest and the studies related with it (so-called bioinformatics) occupied an important position in the field of biological and medical research. The microarray is a revolutionary technology which enables us to monitor several thousands of genes simultaneously and thus to gain an insight into the phenomena in the human body (e.g. the mechanism of cancer progression) at the molecular level. To obtain useful information from such gene expression measurements, it is essential to analyze the data with appropriate techniques. However the high-dimensionality of the data can bring about some problems such as curse of dimensionality and singularity problem of matrix computation, and hence makes it difficult to apply conventional data analysis methods. Therefore, the development of method which can effectively treat the data becomes a challenging issue in the field of computational biology. This research focuses on the gene selection and classification for cancer subtype discrimination based on gene expression (microarray) data.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1042-1047
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• 2004

Voltage control is an essential part of the electric energy transmission and distribution system to maintain proper voltage limit at the consumer's terminal. Besides the generating units that provide the basic voltage control, there are many additional voltage-controlling agents e.g., shunt capacitors, shunt reactors, static VAr compensators, regulating transformers mentioned in [1], [2]. The most popular one, among all those agents for controlling voltage levels at the distribution and transmission system, is the on-load tap changer transformer. It serves two functions-energy transformation in different voltage levels and the voltage control. Artificial Neural Network (ANN) has been realized as a convenient tool that can be used in controlling the on load tap changer in the distribution transformers. Usage of the ANN in this area needs suitable training and testing data for performance analysis before the practical application. This paper briefly describes a procedure of processing the data to train an Artificial Neural Network (ANN) to control the tap changer operating decision of parallel transformers for a closed primary bus. The data set are used to train a two layer ANN using three different neural net learning algorithms, namely, Standard Backpropagation [3], Bayesian Regularization [4] and Scaled Conjugate Gradient [5]. The experimental results are presented including performance analysis.

• Journal of The Korean Association of Information Education
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• v.15 no.3
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• pp.493-503
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• 2011

The Korea Ministry of Education, Science and Technology (MEST) determines admission quota of cyber universities. MEST's decision is made based on each university's physical and administrative capacity for handling admission numbers. However, the unique characteristics of cyber universities (e.g., online teaching and learning environments) are not considered in MEST's current decision process. MEST also lacks specifics in their policies that are required to ensure university's autonomous control for admission number as well as learners' rights and quality assurance. This study intended to improve decision making process on admission quota of cyber universities so as to increase quality assurance of education. The alternative admission quota decision frameworks have been formulated based on (a) the analysis of the current practices of cyber universities, (b) focus group interviews, and (c) recommendations of the expert.

• Journal of Korean Academy of Nursing Administration
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• v.21 no.2
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• pp.154-163
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• 2015

Purpose: This descriptive study was done to identify factors that influence stress related to clinical practice for nursing students. Methods: Structured questionnaires were used to collect data from 278 students from two nursing colleges located in G metropolitan city and one nursing college in C region. Results: The factors that most influenced stress for the nursing students during their clinical practice were critical thinking disposition, clinical competence, year, and gender. Especially, the result showed that higher critical thinking disposition and clinical competence correlated with lower stress in clinical practice. Conclusion: The results indicate that improving nursing students' critical thinking ability and clinical competence would help to relieve stress during clinical practice and increase the ability to cope with stress efficiently. The development of a variety of teaching and learning strategies and education in both theoretical and clinical practice education would be necessary to achieve this goal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2748-2760
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• 2018

This paper proposes an audio event classification method based on convolutional neural networks (CNNs). CNN has great advantages of distinguishing complex shapes of image. Proposed system uses the features of audio sound as an input image of CNN. Mel scale filter bank features are extracted from each frame, then the features are concatenated over 40 consecutive frames and as a result, the concatenated frames are regarded as an input image. The output layer of CNN generates probabilities of audio event (e.g. dogs bark, siren, forest). The event probabilities for all images in an audio segment are accumulated, then the audio event having the highest accumulated probability is determined to be the classification result. This proposed method classified thirty audio events with the accuracy of 81.5% for the UrbanSound8K, BBC Sound FX, DCASE2016, and FREESOUND dataset.

• Journal of Korean Academy of Nursing
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• v.47 no.2
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• pp.222-232
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• 2017

Purpose: This study was done to develop a children's sex education program for the parents of lower elementary grade students and to evaluate its effects on sexual knowledge, gender role attitude, parent efficacy for child's sex education, and marital consistency. Methods: A quasi-experimental with a non-equivalent control group pretest-posttest design was used. The participants were 29 couples (58 parents, experimental group=28, control group=30) from G city. The 5-week (5-session) program was developed based on 'A theory of protection: parents as sex educators' and used the case-based small group learning method. Data were collected during July and August 2015. The characteristics of the program developed in the present study were a theoretical-based, client-centered, multi-method. Results: After the intervention, the experimental group showed a significant improvement in sexual knowledge, gender role attitudes, parent efficacy for child's sex education, and marital consistency, compared to the control group. The effect sizes of the program were .64 (knowledge), .65 (gender role attitudes), and .68 (parent efficacy). Conclusion: The results of this study provided implications for the parents as effective sex educator and the role expansion of school health nurses.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.452-455
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• 2005

The cutting characteristics of hardened steel by a PCBN tool is investigated with respect to workpiece surface roughness, cutting force and tool flank wear of the vision system. Backpropagation neural networks (BPNs) were used for detection of tool wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output was the tool wear state which was either usable or failure. Hard turning experiments with various spindle rotational speed and feed rates were carried out. The learning process was performed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.86-94
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• 2003

Electronically available biological literature has been accumulated exponentially in the course of time. So, researches on automatically acquiring knowledge from these tremendous data by text mining technology become more and more prosperous. However, most of the previous researches are technology oriented and are not well focused in practical extraction target, hence result in low performance and inconvenience for the bio-researchers to actually use. In this paper, we propose a more biology oriented target domain specific text mining system, that is, POSTECH bio-text mining system (POSBIOTM), for signal transduction pathway extraction, especially for G protein-coupled receptor (GPCR) pathway. To reflect more domain knowledge, we specify the concrete target for pathway extraction and define the minimal pathway domain ontology. Under this conceptual model, POSBIOTM extracts interactions and entities of pathways from the full biological articles using a machine learning oriented extraction method and visualizes the pathways using JDesigner module provided in the system biology workbench (SBW) [14]

• Geomechanics and Engineering
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• v.11 no.3
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• pp.361-372
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• 2016

The soil-concrete interface shear strength, although has been extensively studied, is still difficult to predict as a result of the dependence on many factors such as normal stresses, surface roughness, particle sizes, moisture contents, dilation angles of soils, etc. In this study, a well-known rigorous statistical learning approach, namely the least squares support vector machine (LS-SVM) realized in a ubiquitous spreadsheet platform is firstly used in estimating the soil-structure interface shear strength. Instead of studying the complicated mechanism, LS-SVM enables to explore the possible link between the fundamental factors and the interface shear strengths, via a sophisticated statistic approach. As a preliminary investigation, the authors study the expansive soils that are found extensively in most countries. To reduce the complexity, three major influential factors, e.g., initial moisture contents, initial dry densities and normal stresses of soils are taken into account in developing the LS-SVM models for the soil-concrete interface shear strengths. The predicted results by LS-SVM show reasonably good agreement with experimental data from direct shear tests.