• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.1077-1082
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• 2001

기존의 타이어를 사용한 중량충격음에 대한 차음성능 평가방법의 타당성을 살펴보기 위하여 청감실험에 의한 감성적 반응결과와 L등급 및 Leq에 의한 평가 결과를 비교 분석하였다. 동일한 바닥충격원에 대한 분석결과 L등급평가 보다 Leq에 의한 평가가 청감실험의 반응에 잘 대응하는 것으로 나타났다. 또한, Zwicker parameters 중 Loudness와 Unbiased Annoyance는 청감실험과 가장 유사한 경향을 보였다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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• pp.485-490
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• 2003

RP(Rapid Prototyping) has been used in the various industrial applications. This paper presents the optimization techniques fur fabricated 3D model design using RP machine for the medical field. Once the original brain model data are obtained from 2D slices of MRI/CT machine, the data can be modeled as an optimal ellipse. The objective of this study includes optimization of fabrication time and surface roughness using the adaptive slicing method. It can reduce fabrication time without losing surface roughness quality by accumulating the slices with variable thickness. According to the parameter tuning and synthesis of its effect, more suitable parameter values can be obtained by enhanced 3D brain model fabrication. Therefore, accurate 3D brain model fabricated by RP machine can enable a surgeon to perform pre-operation. to make a decision for the operation sequence and to perceive the 3D positions in prototype, before delicate operation of actual surgery.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.829-835
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• 2012

When the input features are generated by factors in a classification problem, it is more meaningful to identify important factors, rather than individual features. The $F_{\infty}$-norm support vector machine(SVM) has been developed to perform automatic factor selection in classification. However, the $F_{\infty}$-norm SVM may suffer from estimation inefficiency and model selection inconsistency because it applies the same amount of shrinkage to each factor without assessing its relative importance. To overcome such a limitation, we propose the adaptive $F_{\infty}$-norm ($AF_{\infty}$-norm) SVM, which penalizes the empirical hinge loss by the sum of the adaptively weighted factor-wise $L_{\infty}$-norm penalty. The $AF_{\infty}$-norm SVM computes the weights by the 2-norm SVM estimator and can be formulated as a linear programming(LP) problem which is similar to the one of the $F_{\infty}$-norm SVM. The simulation studies show that the proposed $AF_{\infty}$-norm SVM improves upon the $F_{\infty}$-norm SVM in terms of classification accuracy and factor selection performance.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.17-24
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• 2023

Based on the problem of 'Tatanic - Machine Learning from Disaster', a representative competition of Kaggle that presents challenges related to data science and solves them, we want to see how data preprocessing and model construction affect prediction accuracy and score. We compare and analyze the features by selecting seven top-ranked solutions with high scores, except when using redundant models or ensemble techniques. It was confirmed that most of the pretreatment has unique and differentiated characteristics, and although the pretreatment process was almost the same, there were differences in scores depending on the type of model. The comparative analysis study in this paper is expected to help participants in the kaggle competition and data science beginners by understanding the characteristics and analysis flow of the preprocessing methods of the top score participants.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.3
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• pp.274-281
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• 2013

Field measurement method of heavy/soft impact sound pressure level which is regulated in JIS and ISO has been using in Korea, Japan and Canada. It is reported that heavy/soft impact sound pressure level was varied by the sound field condition of receiving room such as sound absorption power and room volume. In this study, it is checked that heavy/soft impact sound pressure level was affected by the receiving sound field condition. Rubber ball and bang machine sound pressure level was measured in the vertically connected reverberation chamber. In oder to check the effect of receiving sound field on heavy/soft impact sound pressure, sound absorption power was changed with polyester sound absorption blankets with air space and glass wool. The reverberation time at 1 kHz band was changed from 10 s to 0.2 s by sound absorption material. Rubber ball sound pressure level measured without sound absorption material was 58 dB in $L_{i,Fmax,AW}$, but the level was 46 dB with sound absorption treatment. From this result, it is confirmed that sound field correction method is needed in the heavy/soft impact sound pressure level measurement method using bang machine and rubber ball.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.3
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• pp.257-267
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• 2005

The real-time operating system(RTOS) simulator, one of the tools provided by RTOS development environment, allows users to develop and debug application programs even before the target hardware is ready. Thus, most of commercial RTOS development environments provide with RTOS simulator for the purpose. But they are implemented to simulate only functional aspects on a host system, so that it is not possible to estimate execution time of application programs on the target hardware. Since the real-time system has to complete program executions in predetermined time, the RTOS simulator that can estimate the execution time is yeW useful in the development phase. In this study, we develop a machine instruction-level RTOS simulator that is able to estimate execution time of application programs on a target hardware, and prove its functionality and accuracy by using test .programs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.5
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• pp.413-420
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• 2021

This study is about a machine learning model for predicting insufficient parameters through other parameters when one of the collected parameters is insufficient. A regression model was created to predict time, temperature, humidity, CO2, and light quantity data through the machine learning regression analysis function in Matlab. In addition, the three models with the lowest RMSE values for each parameter were selected and verified. For verification, the predicted values were obtained by applying the test data to the prediction model derived from each parameter, and the correlation coefficient and error average between the measured values and the obtained predicted values were obtained and then compared.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.177-188
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• 2022

The support vector machine (SVM) has been successfully applied to various classification areas with a high level of classification accuracy. However, it is infeasible to use the SVM in analyzing massive data because of its significant computational problems. When analyzing imbalanced data with different class sizes, furthermore, the classification accuracy of SVM in minority class may drop significantly because its classifier could be biased toward the majority class. To overcome such a problem, we propose the DOC-SVM method, which uses divide-oversampling and conquers techniques. The proposed DOC-SVM divides the majority class into a few subsets and applies an oversampling technique to the minority class in order to produce the balanced subsets. And then the DOC-SVM obtains the final classifier by aggregating all SVM classifiers obtained from the balanced subsets. Simulation studies are presented to demonstrate the satisfactory performance of the proposed method.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.158-166
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• 2004

Predicting the destination of a protein in a cell gives valuable information for annotating the function of the protein. Recent technological breakthroughs have led us to develop more accurate methods for predicting the subcellular localization of proteins. The most important factor in determining the accuracy of these methods, is a way of extracting useful features from protein sequences. We propose a new method for extracting appropriate features only from the sequence data by computing pairwise sequence alignment scores. As a classifier, support vector machine (SVM) is used. The overall prediction accuracy evaluated by the jackknife validation technique reach 94.70% for the eukaryotic non-plant data set and 92.10% for the eukaryotic plant data set, which show the highest prediction accuracy among methods reported so far with such data sets. Our numerical experimental results confirm that our feature extraction method based on pairwise sequence alignment, is useful for this classification problem.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.12
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• pp.95-102
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• 2014

This paper proposes a coordinated control for an independent multi-phase transverse flux linear synchronous motor (IM-TFLSM) based on magnetic levitation. The stator structures of the IM-TFLSM are composed of a two set, which has independent three-phase windings and a double-sided air-gap as opposed to the conventional Y-connected three-phase linear motors. A suitable control algorithm is necessary to operate the applied linear machine. This study proposes a coordinated control algorithm for adjusting the mover air-gap and thrust force of the IM-TFLSM in order to maintain air-gap and phase shifted current control of the independent 3-phase modules. In addition, the principle of operation and its special structures are described in detail and the validity and effectiveness of the control algorithm is verified through multiple experimental results.