• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.11a
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• pp.85-88
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• 2000

FRP has been used very much as high strength core materials for insulators because of its high strength and good insulation properties. In this study cantilever, tension and torsion stress were simulation along to the unidirection glass fiber. In addition, FRP was made by pultrusion method. This paper proposed the procedure of the finite element model updating and pretest using the commercial finite element code MSC. Nastran. To enhance the efficiency of experimental modal analysis, we proposed the process which is the selection of the locations and the number of measurement points for pre-test.

• Transactions of the Society of Information Storage Systems
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• v.2 no.2
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• pp.144-149
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• 2006

Dynamic L/UL(Load/Unload) system has many merits. but it may happen an undesirable collision during the dynamic loading process. In this paper, the dynamics of negative pressure pico-slider was investigated through numerical simulation during the loading process. A simplified L/UL model for the suspension system has been presented and a simulation code has been developed to analyze the motion of the slider. A slider design has been simulated at various disk rotating speeds, skew angles of slider. We can decrease the possibility of collision and smoothen the loading process for a given slider-suspension design by selection an optimal rpm and pre-skew angle.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.235-239
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• 2010

MicroRNA는 RNA로 전사된 유전자와의 상보결합을 통해 유전자 발현을 억제하는 조절인자이다. MicroRNA 생성과정에서 pre-microRNA의 3' 또는 5' 부근의 strand가 선택되어 mature 시퀀스가 되고 유전자 조절에 직접 작용하게 된다. 하지만 어떤 특징을 가진 strand가 선택 되는지에 대한 정확한 메커니즘은 아직 연구되어 있지 않다. 본 논문에서는 microRNA 시퀀스 정보를 바탕으로 하이퍼망을 구성하여 strand 선택 예측 모델을 구축하였다. 실험 결과 하이퍼망 학습을 통해 microRNA strand 선택에 중요한 영향을 미치는 시퀀스 특징을 찾을 수 있었고, strand 선택을 높은 정확도로 예측할 수 있음을 확인하였다.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.809-819
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• 2020

Gender prediction accuracy increases as convolutional neural network (CNN) architecture evolves. This paper compares voting and ensemble schemes to utilize the already trained five CNN models to further improve gender prediction accuracy. The majority voting usually requires odd-numbered models while the proposed softmax-based voting can utilize any number of models to improve accuracy. The ensemble of CNN models combined with one more fully-connected layer requires further tuning or training of the models combined. With experiments, it is observed that the voting or ensemble of CNN models leads to further improvement of gender prediction accuracy and that especially softmax-based voters always show better gender prediction accuracy than majority voters. Also, compared with softmax-based voters, ensemble models show a slightly better or similar accuracy with added training of the combined CNN models. Softmax-based voting can be a fast and efficient way to get better accuracy without further training since the selection of the top accuracy models among available CNN pre-trained models usually leads to similar accuracy to that of the corresponding ensemble models.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.171-179
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• 2007

Hyperspectral images have shown a great potential for the applications in resource management, agriculture, mineral exploration and environmental monitoring. However, due to the large volume of data, processing of hyperspectral images faces some difficulties. This paper introduces the development of an image processing tool (HYVIEW) that is particularly designed for handling hyperspectral image data. Current version of HYVIEW is dealing with efficient algorithms for displaying hyperspectral images, selecting bands to create color composites, and atmospheric correction. Three band-selection schemes for producing color composites are available based on three most popular indexes of OIF, SI and CI. HYVIEW can effectively demonstrate the differences in the results of the three schemes. For the atmospheric correction, HYVIEW utilizes a pre-calculated LUT by which the complex process of correcting atmospheric effects can be performed fast and efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.929-944
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• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

• Genomics & Informatics
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• v.16 no.4
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• pp.32.1-32.7
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• 2018

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.221-235
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• 2022

Among nondestructive damage detection methods, impedance-based methods have been recognized as an effective technique for damage identification in many kinds of structures. This paper proposes a method to detect cracks in metal structures by combining electro-mechanical impedance (EMI) responses and artificial neural networks (ANN). Firstly, the theories of EMI responses and impedance-based damage detection methods are described. Secondly, the reliability of numerical simulations for impedance responses is demonstrated by comparing to pre-published results for an aluminum beam. Thirdly, the proposed method is used to detect cracks in the beam. The RMSD (root mean square deviation) index is used to alarm the occurrence of the cracks, and the multi-layer perceptron (MLP) ANN is employed to identify the location and size of the cracks. The selection of the effective frequency range is also investigated. The analysis results reveal that the proposed method accurately detects the cracks' occurrence, location, and size in metal structures.

• Journal of Sensor Science and Technology
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• v.30 no.4
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• pp.223-228
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• 2021

This work presents a low-power CMOS image sensor (CIS) with a multi-column-parallel (MCP) readout structure while focusing on improving its performance compared to previous works. A delta readout scheme that utilizes the image characteristics is optimized for the MCP readout structure. By simply alternating the MCP readout direction for each row selection, additional memory for the row-to-row delta readout is not required, resulting in a reduced area of occupation compared to the previous work. In addition, the bias current of a pre-amplifier in a successive approximate register (SAR) analog-to-digital converter (ADC) changes according to the operating period to improve the power efficiency. The prototype CIS chip was fabricated using a 0.18-㎛ CMOS process. A 160 × 120 pixel array with 4.4 ㎛ pitch was implemented with a 10-bit SAR ADC. The prototype CIS demonstrated a frame rate of 120 fps with a total power consumption of 1.92 mW.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.472-475
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• 2020

실생활의 사례를 바탕으로 생성된 여러 분야의 데이터셋을 기계학습 (Machine Learning) 문제에 적용하고 있다. 정보보안 분야에서도 사이버 공간에서의 공격 트래픽 데이터를 기계학습으로 분석하는 많은 연구들이 진행 되어 왔다. 본 논문에서는 공격 데이터를 유형별로 정확히 분류할 때, 실생활 데이터에서 흔하게 발생하는 데이터 불균형 문제로 인한 분류 성능 저하에 대한 해결방안을 연구했다. 희소 클래스 관점에서 데이터를 재구성하고 기계학습에 악영향을 끼치는 특징들을 제거하고 DNN(Deep Neural Network) 모델을 사용해 분류 성능을 평가했다.