• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.8
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• pp.1002-1009
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• 2012

This paper shows the research about radar target recognition using the measured radar signals from MSU(Michgan State University) bistatic radar system. In this research, we first did the bistatic measurements at $30^{\circ}$, $60^{\circ}$, $90^{\circ}$ using F-14, Mig-29, and F-22 scale models. Then, we extract the target feature vectors using time-frequency analysis methods such as STFT(Short Time Fourier Transform) and CWT(Continous Wavelet Transform) and perform the target classification test using MLP(Multi-layerd Perceptron) neural network. The results show that the target classification performance is too much dependent on the bistatic angles and the best performance is obtained at the $60^{\circ}$ bistatic angle.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.181-186
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• 2020

The purpose of this study is to empirically examine the factors affecting the binge-watching behaviors of OTT service users by using a multi-layer perceptron (MLP) artificial neural network. All samples (n=1,000) were collected from 'A survey on user awareness in OTT service' published by a Media Research Center of the Korea Press Foundation in 2018. Our research model includes one dependent variable which is binge-watching behaviors on OTT service and five independent variables such as gender, age, frequency of service usage, users' satisfaction with content recommendation algorithm, and content types mainly consumed. Our findings demonstrate that age, frequency of service usage, users' satisfaction with content recommendation algorithms, and certain types of contents (e.g., Korean dramas, Korean films, and foreign dramas) were found to be highly related to binge-watching behavior on OTT services.

• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2017-2024
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• 1997

The main focus of this paper is the recognition of printed Hangul documents in terms of typeface, character size and character slope for IICS(Intelligent Image Communication System). The fixed-size blocks extracted from documents are analyzed in frequency domain for the typeface classification. The vertical pixel counts and projection profile of bounding box are used for the character size classification and the character slope classification, respectively. The MLP with variable hidden nodes and error back-propagation algorithm is used as typeface classifier, and Mahalanobis distance is used to classify the character size and slope. The experimental results demonstrated the usefulness of proposed system with the mean rate of 95.19% in typeface classification. 97.34% in character size classification, and 89.09% in character slope classification.

• The Korean Journal of Physiology and Pharmacology
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• v.2 no.3
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• pp.369-376
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• 1998

This report shows that hydroxyl radical, generated by a Fenton reaction involving adenosine $5'-diphosphate/Fe^{2+}$ complex ($5-15\;{\mu}M$) and $H_2O_2$ ($2\;{\mu}M$), induced differentiation of HL-60 cells in a dose- and time-dependent manner. This is evidenced by the increases in 12-O-tetradecanoylphorbol 13-acetate- and fMLP-stimulated superoxide production capability. The cells exposed to hydroxyl radical for defined periods (24∼96 hr) continued to differentiate even after the hydroxyl radical generating system had been removed. The differentiated cells displayed fMLP-stimulated calcium mobilization and increased expression of myeloid-specific antigen CD11b and CD14. The extent of the differentiation was markedly reduced by desferrioxamine ($100\;{\mu}M$), dimethylthiourea (5 mM), N,N'-diphenyl-1,4-phenylenediamine ($2\;{\mu}M$), and N-acetyl-L-cysteine (5 mM). The induction of differentiation by hydroxyl radical was enhanced by 3-isobutyl-1-methylxanthine ($200\;{\mu}M$) and Ro-20-1724 ($8\;{\mu}M$), and inhibited by dipyridamole (2 ${\mu}M$). These results suggest that hydroxyl radicals may induce commitment of HL-60 cells to differentiate into more mature cells of myelomonocytic lineage through specific signal-transduction pathway that is modulated by phosphodiesterase inhibitors.

• The Korean Journal of Physiology and Pharmacology
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• v.8 no.1
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• pp.43-50
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• 2004

Ischemia followed by reperfusion in the presence of polymorphonuclear leukocytes (PMNs) results in a marked cardiac contractile dysfunction. Amrinone, a specific inhibitor of phosphodiesterase 3, has an antioxidant activity against PMNs. Therefore, we hypothesized that amrinone could attenuate PMNs-Induced cardiac dysfunction by suppression of reactive oxygen species (ROS) produced fby PMNs. In the present study, we examined the effects of amrinone on isolated ischemic (20 min) and reperfused (45 min) rat hearts perfused with PMNs. Amrinone at $25\;{\mu}M$, given to hearts during the first 5 min of reperfusion, significantly improved coronary flow, left ventricular developed pressure (P<0.001), and the maximal rate of development of left ventricular developed pressure (P<0.001), compared with ischemic/reperfused hearts perfused with PMNs in the absence of amrinone. In addition, amrinone significantly reduced myeloperoxidase activity by 50.8%, indicating decreased PMNs infiltration (p< 0.001). Superoxide radical and hydrogen peroxide production were also significantly reduced in fMLP- and PMA-stimulated PMNs pretreated with amrinone. Hydroxyl radical was scavenged by amrinone. fMLP-induced elevation of $[Ca^{2+}]_i$ was also inhibited by amrinone. These results provide evidence that amrinone can significantly attenuate PMN-induced cardiac contractile dysfunction in the ischemic/reperfused rat heart via attenuation of PMNs infiltration into the myocardium and suppression of ROS release by PMNs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.730-737
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• 2018

With the increasing number of health consumers aiming for a happy quality of life, the O2O medical marketing market is activated by choosing reliable health care facilities and receiving high quality medical services based on the medical information distributed on web's blog. Because unstructured text data used on the Internet, mobile, and social networks directly or indirectly reflects authors' interests, preferences, and expectations in addition to their expertise, it is difficult to guarantee credibility of medical information. In this study, we propose a blog reading system that provides users with a higher quality medical information service by classifying medical information blogs (medical blog, ad blog) using bigdata and MLP processing. We collect and analyze many domestic medical information blogs on the Internet based on the proposed big data and machine learning technology, and develop a personalized health information recommendation system for each disease. It is expected that the user will be able to maintain his / her health condition by continuously checking his / her health problems and taking the most appropriate measures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.20-26
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• 2019

Due to the human influence of particulate matter, various studies are being conducted to predict it using past data measured in the atmospheric environment monitoring network. However, it is difficult to precisely set the measurement environment and detailed conditions of the previously designed predictive model, and it is necessary to design a new predictive model based on the existing research results because of the problems such as the missing of the weather data. In this paper, as a previous study for particulate matter prediction, the conformity of the algorithm for particulate matter prediction was evaluated by designing the prediction model through the multiple linear regression and the artificial neural network, which are machine learning algorithms. As a result of the prediction performance comparison through RMSE, 18.13 for the MLR model and 14.31 for the MLP model, and the artificial neural network model was more conformable for predicting the particulate matter concentration.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.23-29
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• 2019

In this paper, the mood of images was classified into eight categories through a deep convolutional neural network and video was automatically generated using proper background music. Based on the collected image data, the classification model is learned using a multilayer perceptron (MLP). Using the MLP, a video is generated by using multi-class classification to predict image mood to be used for video generation, and by matching pre-classified music. As a result of 10-fold cross-validation and result of experiments on actual images, each 72.4% of accuracy and 64% of confusion matrix accuracy was achieved. In the case of misclassification, by classifying video into a similar mood, it was confirmed that the music from the video had no great mismatch with images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.23-28
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• 2021

Data analysis research using deep learning has recently been studied in various field. In this paper, we conduct a GNSS (Global Navigation Satellite System)-based meteorological study applying deep learning by estimating the ZWD (Zenith tropospheric Wet Delay) through MLP (Multi-Layer Perceptron) and LSTM (Long Short-Term Memory) models. Deep learning models were trained with meteorological data and ZWD which is estimated using zenith tropospheric total delay and dry delay. We apply meteorological data not used for learning to the learned model to estimate ZWD with centimeter-level RMSE (Root Mean Square Error) in both models. It is necessary to analyze the GNSS data from coastal areas together and increase time resolution in order to estimate ZWD in various situations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.584-590
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• 2020

Long-term use of digital screens in daily life can lead to computer vision syndrome including symptoms such as eye strain, dry eyes, and headaches. To prevent computer vision syndrome, it is important to limit screen usage time and take frequent breaks. There are a variety of applications that can help users know the screen usage time. However, these apps are limited because users see various screens such as desktops, laptops, and tablets as well as smartphone screens. In this paper, we propose and evaluate machine learning-based models that detect the screen device in use using color, IMU and lidar sensor data. Our evaluation shows that neural network-based models show relatively high F1 scores compared to traditional machine learning models. Among neural network-based models, the MLP and CNN-based models have higher scores than the LSTM-based model. The RF model shows the best result among the traditional machine learning models, followed by the SVM model.