• Proceedings of the KIEE Conference
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• 1999.07a
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• pp.259-261
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• 1999

In this paper, we investigated the frequency dependences of AC loss and the magnetic properties of a superconductor using magnetization method. We used the YBCO bulk superconductor sample which has a square-shaped cross sectional area of $6mm{\times}6mm$. and 43mm-long in axial direction. The applied AC magnetic field was parallel to the direction of sample axis. It is observed that YBCO bulk has the diamagnetic properties. AC loss calculation of YBCO bulk superconductor was performed by evaluating the total area of magnetization traces. We performed two cases of experiments, one for $B_m<B_p$ and the other for $B_m<B_p$. The experiments showed that the AC loss dependences on frequency were different in each mode, as was indicated in other publications.

• Progress in Superconductivity and Cryogenics
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• v.19 no.3
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• pp.1-7
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• 2017

It is required to reduce the cost of superconducting cable to realize a superconducting DC power network that covers a wide area in order to utilize renewable energy. In this paper a new concept of innovative cable is introduced that can enhance the current-carrying capacity even though the same superconducting tape is used. Such a cable can be realized by designing an optimal winding structure in such a way that the angle between the tape and magnetic field becomes small. This idea was confirmed by preliminary experiments for a single layer model cable made of Bi-2223 tapes and REBCO coated conductors. Experiments of three and four layer cables of practical sizes were also done and it was found that the current-carrying capacity increased as theoretically predicted. If the critical current properties of commercial superconducting tapes are further improved in a parallel magnetic field, the enhancement will become pronounced and this technology will surely contribute to realization of superconducting DC power network.

• Genomics & Informatics
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• v.9 no.2
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• pp.89-91
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• 2011

DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip. And the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties such as cancer. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. The Particle Swarm Optimization algorithm which was recently proposed is a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed is efficient in terms of execution time for clustering DNA chip data, and thus be used to extract valuable information such as cancer related genes from DNA chip data with high cluster accuracy and in a timely manner.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.111-116
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• 2017

This paper proposes a data mining approach to predicting stock price direction. Stock market fluctuates due to many factors. Therefore, predicting stock price direction has become an important issue in the field of stock market analysis. However, in literature, there are few studies applying data mining approaches to predicting the stock price direction. To contribute to literature, this paper proposes comparing single classifiers and ensemble classifiers. Single classifiers include logistic regression, decision tree, neural network, and support vector machine. Ensemble classifiers we consider are adaboost, random forest, bagging, stacking, and vote. For the sake of experiments, we garnered dataset from Korea Stock Exchange (KRX) ranging from 2008 to 2015. Data mining experiments using WEKA revealed that random forest, one of ensemble classifiers, shows best results in terms of metrics such as AUC (area under the ROC curve) and accuracy.

• ETRI Journal
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• v.33 no.6
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• pp.914-923
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• 2011

This paper concerns robust and reliable speaker model training for text-independent speaker verification. The baseline speaker modeling approach is the Gaussian mixture model (GMM). In text-independent speaker verification, the amount of speech data may be different for speakers. However, we still wish the modeling approach to perform equally well for all speakers. Besides, the modeling technique must be least vulnerable against unseen data. A traditional approach for GMM training is expectation maximization (EM) method, which is known for its overfitting problem and its weakness in handling insufficient training data. To tackle these problems, variational approximation is proposed. Variational approaches are known to be robust against overtraining and data insufficiency. We evaluated the proposed approach on two different databases, namely KING and TFarsdat. The experiments show that the proposed approach improves the performance on TFarsdat and KING databases by 0.56% and 4.81%, respectively. Also, the experiments show that the variationally optimized GMM is more robust against noise and the verification error rate in noisy environments for TFarsdat dataset decreases by 1.52%.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.61-64
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• 2001

This paper describes a basic study for the classification of cognitive mental states as a basic research of a human-computer interface technique. To recognize the mental states, we obtained 22 subjects’brain waves in course of two types of experiments. One of the experiments is to choose an answer among yes, no or reject buttons, to underlying questions and the other is to select an icon displayed in a monitor screen. After acquiring the brain wave signals, we construct a feature set with the percent power increase for a given segment with respect to that of the reference period. The linear discriminative algorithm is used to classify the cognitive yes/no mental states.

• Journal of the Korea Society of Computer and Information
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• v.22 no.6
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• pp.69-77
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• 2017

The information related to legislation is massive, and it takes much time and effort to manually build the legislation ontology. Thus, studies on machine-based automated building methods are underway. However, the studies to automatically construct such systems focus on using TBox construction, and those based on automated ABox construction, which corresponds to instances, theoretical systems and data building cases, has not yet been sufficiently developed. Therefore, this paper suggests using a semi-automatic ABox construction method based on sentence patterns to automatically build the ontology for the legislation of the Republic of Korea. Precision and Recall experiments were conducted to further discuss the performance of the suggested method. These experiments provide a comparison between the manual classification, and the triples built by the machines of the legal information by assessing the corresponding numerical values.

• Journal of the korean Society of Automotive Engineers
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• v.15 no.1
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• pp.107-119
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• 1993

The objective of this study is to analyze the static and dynamic characteristics of automotive rubber components by computer simulation. Bush / rectangular type engine mounts and wind shield weather strip are analyzed by using the commercial code ABAQUS and the results are verified by experiments. Large deformation static response is analyzed in order to get the information about the deformation pattern and static stiffness of engine mounts, and about the seperation force of wind shield weather strip from body. The isothermal steady-state dynamic response of components which have been subjected to an initial static pre-load is analyzed for the dynamic stiffness of engine mount rubber components. There are good agreements between simulation and experiments. So it is possible to apply the computer simulation to the design of automotive rubber components.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2603-2605
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• 2001

In this paper, we introduce the concept and construction of a web-based bidirectional remote laboratory system. In this configuration, students build and test circuits using internet at home, and the circuit components and experimental equipments at the school are interconnected following student's design. This means that it is possible to choose components, build circuits, connect experiment equipments, control the equipment. and receive measurement results at home in real-time. Labview and Java are used to control this system efficiently and to provide user-friendly environments. Computer-based function generator, DMM, and oscilloscope are used and switch module is used to change interconnections. It was found that a system adopting computer-based instruments, which include PXI/CompactPCI modules and PC plug-in board, provide better performance than that adopting stand-alone instruments connected with GPIB. It is believed that, if this system is installed, the real experiments, not the virtual experiments based on simulation, can be done at home effectively, resulting in revolutional change in engineering education.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.8 s.185
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• pp.119-126
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• 2006

Kriging model is widely used as design DACE(analysis and computer experiments) model in the field of engineering design to accomplish computationally feasible design optimization. In this paper, the optimization of gate valve was performed using Kriging based approximation model. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. In addition, we describe the definition, the prediction function and the algorithm of Kriging method and examine the accuracy of Kriging by using validation method.