• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.27-33
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• 2008

The clustering of gene expression data is used to analyze the results of microarray studies. This clustering is one of the frequently used methods in understanding degrees of biological change and gene expression. In biological research, MCL algorithm is an algorithm that clusters nodes within a graph, and is quick and efficient. We have modified the existing MCL algorithm and applied it to microarray data. In applying the MCL algorithm we put forth a simulation that adjusts two factors, namely inflation and diagonal tent and converted them by making use of Markov matrix. Furthermore, in order to distinguish class more clearly in the modified MCL algorithm we took the average of each row and used it as a threshold. Therefore, the improved algorithm can increase accuracy better than the existing ones. In other words, in the actual experiment, it showed an average of 70% accuracy when compared with an existing class. We also compared the MCL algorithm with the self-organizing map(SOM) clustering, K-means clustering and hierarchical clustering (HC) algorithms. And the result showed that it showed better results than ones derived from hierarchical clustering and K-means method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.8
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• pp.758-765
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• 2016

This paper proposes a novel method for the recognition of the inverse synthetic aperture radar(ISAR) image of multiple targets flying in formation. Rather than separating the ISAR image of each target, the proposed method combines an ISAR image obtained by fusing the ISAR images in the training database. Fusion is conducted by optimizing the non-linear problem whose parameters are the aspect angle and the target location. Assuming that the aspect angle is properly estimated, the proposed method estimates the number of the targets and their locations by optimizing the template matching using PSO. In simulations using the F-16 scale model, the efficiency of the proposed method was demonstrated by yielding the ISAR image identical to that of targets in formation.

• Communications of Mathematical Education
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• v.27 no.3
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• pp.341-356
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• 2013

This research is about storytelling in middle school geometry. This study is basic research about actualizing mathematical teaching and learning based on storytelling that is raised for reforming school mathematics education. In order to develop the mathematics textbook model, development of procedures and methods were extracted from the literature. And mathematics textbook model were developed in accordance with the process and methods. Examples are presented in terms of the development of material with 'story as a communication tool', 'familiar story as a script' 'universality of the world or simulation of life as a story', 'story as a means to foster creativity: story making'. Through the results of this study, we are also able to check the possibility of storytelling in mathematics class. And this study will be the foundation for teaching and learning using storytelling.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.57-62
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• 2014

This paper proposes the improving the equalization performance using the adaptive modulus concept to the MCMA blind equalizer in order to the reduction of intersymbol interference which occurs in the band limited and time dispersive communication channel. In MCMA blind algorithm, it is possible to reducing the amplitude and phase rotation of intersymbol interference without training sequence, the fixed constant modulus of transmission signal is used. But in proposed algorithm, the modulus are adaptively varies according to the equalizer output signal, then the improved equalization performance were obtained by the computer simulation. For this, the recovered signal constellation that is the output of the equalizer, the convergence performance by MSE, MD (maximum distortion) and residual isi characteristic learning curve were used. The propose algorithm has fairly good performance compared to the traditional MCMA algorithm in the same adaptive equalization algorithm.

• Journal of Digital Convergence
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• v.15 no.8
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• pp.409-416
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• 2017

The game market has not only grown in size of the 10 trillion won but also the form and function of game use. Serious game combines the educational purpose and the fun of the game and develops it so that contents such as national defense. In this study, we analyze the cases of five application areas for the activation of serious games, and develop factors that the serious game should have, we want to see what the characteristics are and how important it is. The results of analysis show that it was analyzed that most serious games of application field were developed as 'experiential games' such as education, learning, experience, and simulation. This means that it is very effective to develop a serious game in consideration of the characteristics that a potential user should prefer a sensible game as well as a clear goal of a serious game in game development.

• Journal of Convergence for Information Technology
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• v.10 no.5
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• pp.60-74
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• 2020

The purpose of this study is to review articles related to disaster training for nursing students and nurses by investigating the contents and effects of research. Electronic databases, including CINAHL, Pubmed and RISS were searched. Papers published only in English or Korean were included. Twelve studies were selected from the 3,588 references screened. Most training programs took less than 8 hours, focusing mostly on the response phase of disaster. Intervention methods included simulation, debriefing, action learning, role play, problem based learning and so on. It was found that disaster training programs have significant positive effects on disaster management competency, disaster knowledge, disaster nursing related self-confidence, and disaster response competency. It was also revealed that multidisciplinary practice would help learners enhance cooperation and collaboration with other team members and foster a positive professional identity. Among the selected articles, the randomized controlled trial(RCT) study was just one. Therefore, RCTs are further needed to verify the effects of such an disaster training. Also, further studies considering the characteristics of department and nursing interventions based on all phases of disaster are needed.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.41-48
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• 2020

The sensor nodes in underwater IoT networks have practical limitations in power supply. Thus, the reduction of power consumption is one of the most important issues in underwater environments. In this regard, AMC(Adaptive Modulation and Coding) techniques are used by using the relation between SNR and BER. However, according to our hands-on experience, we observed that the relation between SNR and BER is not that tight in underwater environments. Therefore, we propose a deep learning based MLP classification model to reflect multiple underwater channel parameters at the same time. It correctly predicts BER with a high accuracy of 85.2%. The proposed model can choose the best parameters to have the highest throughput. Simulation results show that the throughput can be enhanced by 4.4 times higher than the conventionally measured results.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.1-8
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• 2012

In this paper, we propose a new clustering combination based on numerical data driven for rule generation mechanism. In large and complicated space, a clustering method can obtain limited performance results. To overcome the single clustering method problem, hybrid combined methods can solve problem to divided simple cluster estimation. Fundamental structure of the proposed method is combined by mountain clustering and modified Chen clustering to extract detail cluster information in complicated data distribution of non-parametric space. It has automatic rule generation ability with advanced density based operation when intelligent systems including neural networks and fuzzy inference systems can be generated by clustering results. Also, results of the mechanism will be served to information of decision support system to infer the useful knowledge. It can extend to healthcare and medical decision support system to help experts or specialists. We show and explain the usefulness of the proposed method using simulation and results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.10
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• pp.794-801
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• 2017

In this paper, we proposed airborne target recognition using multi-channel combining method in DTV-based passive radar. By combining multi-channel signals, we obtained the HRRP with sufficient range resolution. HRRP was obtained by AR method or zero-padding. From the obtained HRRP, we extracted scattering centers by CLEAN algorithm using the gradient descent. We extracted feature vectors and performed target recognition after training neural network using the extracted feature vectors. To verify performance of proposed methods, we assumed frequency bands of three broadcasting transmitters operated in Korea(Mt. Gwan-ak, Mt. Yong-moon, Kyeon-wol-ak) and used full scale 3D CAD model of four targets. Also we compared the target recognition performance of the proposed method with that of using only single-channel of three broadcasting transmitters. As a result, proposed methods showed better performance than using only single-channel at three broadcasting transmitters.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.243-250
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• 2010

As the wind has become one of the fastest growing renewable energy sources, the key issue of wind energy conversion systems is how to efficiently operate the wind turbines in a wide range of wind speeds. Compared to fixed speed turbines, variable speed wind turbines feature higher energy yields, lower component stress and fewer grid connection power peaks. Generally, measurement of wind speed is required for the control of variable speed wind turbine system. However, wind speed measured by anemometers is not accurate owing to various reasons. In this work, a new control algorithm for variable speed wind turbine system based on Kalman filter which can be used for the estimation of wind speed and artificial neural network which can generate optimum rotor speed is proposed. Also, to verify the feasibility of the proposed scheme, various simulation studies are carried out by using Simulink in Matlab.