• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.846-859
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• 2005

Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering. However, their applications in fault diagnosis of rotating machinery are rather limited. Most of the published papers focus on some special fault diagnoses. This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies. The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.31-33
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• 2009

Face detection using AdaBoost algorithm is capable of processing images rapidly while having high detection rates. It seemed to be the fastest and the most robust and it is still today. Many improvements or extensions of this method have been proposed. However, previous approaches only deal with upright faces. They suffer from limited discriminant capability for rotated faces as these methods apply the same features for both upright and rotated faces. To solve this problem, it is necessary that we rotate input images or make independently trained detectors. However, this can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. This paper proposes a robust algorithm for finding rotated faces within an image. It reduces the computational and sample complexity, by finding common features that can be shared across the classes. And it will be able to apply with multi-class object detection.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.1
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• pp.9-14
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• 2008

This paper presents an observer design methodology for a special class of multi input multi output(MIMO) nonlinear systems. First, we characterize the class of MIMO nonlinear systems with a triangular structure. Also, the observability matrices that plays an important role in proving the convergence of the proposed observer are generalized to MIMO systems. By using the generalized observability matrices, it is shown that under the boundedness conditions of system state and input, the proposed observer guarantees the local exponential convergence to zero of the estimation error.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.42-48
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• 2008

This paper presents an observer design method for a special class of multi input multi output(MIMO) nonlinear systems. First, we characterize the class of MIMO nonlinear systems with a block triangular structure. Also, the observability matrices for SISO nonlinear systems are extended to MIMO systems. By using the generalized observability matrices, it is shown that under the boundedness conditions of system state and input, the proposed observer guarantees the local exponential stability of error dynamics. Finally, its application to induction motor is given to verify the proposed method.

• Mass Spectrometry Letters
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• v.9 no.4
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• pp.110-114
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• 2018

Since accumulation of chemicals in deciduous teeth can occur from the second trimester of fetal development to shedding, a deciduous tooth has been considered as an attractive biomatrix for estimating individual chemical exposures recently. Therefore, detection of organic chemicals from teeth has received an increasing attention in exposomics research. Most previous studies on organic chemical analysis of teeth not only focused on a few targeted chemicals but also ignored potential contaminants from an enamel surface or a dental pulp. Recently, our group started developing a multi-class organic analysis method for deciduous teeth and tried to find a proper incubation condition of tooth materials. Our results showed that incubation with methanolic HCl provided the best performance among tested.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.1
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• pp.14-32
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• 2022

In this paper, we report the results of a conceptual study to develop of a multi-purpose medium-sized UAV that can safely perform missions in harsh maritime environments. In this study, we focused on developing UAVs capable of performing three maritime missions that urgently require the application of medium-sized UAVs: marine ecosystem management, ocean surveillance system, and response to marine accidents. Furthermore improvement points for the above three naval missions using medium-sized UAVs were derived in preparation for the problems of the existing mission performance. Finally, by developing and analyzing the utilization scenario of the medium-class UAV, the required performance suitable for each mission was defined and assigned to the related mission equipment, A new maritime management plan was proposed using the medium-class UAV system equipped with replaceable mission equipment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.367-369
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• 2022

In this paper, the predicted results were confirmed by label mapping for each class while implementing multi-class image classification to confirm accurate results for images in which the trained model failed classification. A CNN model was constructed and trained using Kaggle's Intel Image Classification dataset, and the mapped label values of multiple classes of images and the values classified by the model were compared by label mapping the images of the test dataset.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.551-552
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• 2021

In this paper, we present a multi-class image scene classification method based on transformation learning. ImageNet classifies multiple classes of natural scene images by relying on pre-trained network models on large image datasets. In the experiment, we obtained excellent results by classifying the optimized ResNet model on Kaggle's Intel Image Classification data set.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.701-703
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• 2022

In this paper, we present a multi-class image scene classification method based on map learning. We were able to learn from the convolutional neural network model in the dataset, classify facial scene images of multiclass people, and classify the optimized CNN model into the Google image dataset in the experiment with significant results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.3
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• pp.217-224
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• 2011

This paper proposes DDPT(Dynamic Data Protection Technique) which solves the problem of private information exposure occurring in a dynamic database environment. The DDPT in this paper generates the MAG(Multi-Attribute Generalization) rules using multi-attributes generalization algorithm, and the EC(equivalence class) satisfying the k-anonymity according to the MAG rules. Whenever data is changed, it reconstructs the EC according to the MAC rules, and protects the identification exposure which is caused by the EC change. Also, it measures the information loss rates of the EC which satisfies the ${\ell}$-diversity. It keeps data accuracy by selecting the EC which is less than critical value and enhances private information protection.