• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.12
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• pp.950-958
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• 2013

The Lunar orbiter is expected to be inserted into a ~300km low Earth orbit using Korea Space Launch Vehicle-II(KSLV-II). After the states are successfully determined with obtained tracking data, the Trans Lunar Injection(TLI) burn has to be done at appropriate epoch to send the lunar orbiter to the Moon. In this study, we describe in detail the mission scenario of the Korean lunar orbiter from the launch at NARO Space Center to lunar orbit insertion(LOI) stage following direct transfer trajectory. We investigate the launch window including launch azimuth, delta-V profile according to TLI and LOI burn positions. We also depict the visibility conditions of ground stations and solar eclipse duration to understand the characteristics of the direct transfer trajectory. This paper can be also helpful not only for overall understanding of ${\Delta}V$ trend by changing TOF and coasting time but for selecting launch epoch and control parameters to decrease fuel consumption.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.547-554
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• 2021

In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.2170-2178
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• 2014

In this paper, electroencephalographic (EEG) signal of one among subjects measured biosignal with visual evoked stimuli inducing the concentration was analyzed to detect the changes in the attention status during attention task fulfillment from January to February, 2011. The independent component analysis (ICA) was applied to EEG signals to isolate the attention related innate source signal within the brain and Electroculogram (EOG) artifact from measured EEG signals at the scalp. The consecutive accumulation of short time Fourier transformed (STFT) attention source signal with excluded EOG artifact can enhance the regular depiction of EPOCH graph and spectral color map representing time-varying pattern. The extracted attention indices associated with somatosensory rhythm (SMR: 12-15 Hz), and theta wave (4-7 Hz) increase marginally over time. Throughout experimental observation, the ICA with STFT can be used for the assessment of participants' status of attention.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Journal of The Korean Astronomical Society
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• v.55 no.4
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• pp.111-121
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• 2022

Based on the light an exoplanet blocks from its host star as it passes in front of it during a transit, the mid-transit time can be determined. Periodic variations in mid-transit times can indicate another planet's gravitational influence. We investigate 83 transits of TrES-1 b as observed from 6-inch telescopes in the MicroObservatory robotic telescope network. The EXOTIC data reduction pipeline is used to process these transits, fit transit models to light curves, and calculate transit midpoints. This paper details the methodology for analyzing transit timing variations (TTVs) and using transit measurements to maintain ephemerides. The application of Lomb-Scargle period analysis for studying the plausibility of TTVs is explained. The analysis of the resultant TTVs from 46 transits from MicroObservatory and 47 transits from archival data in the Exoplanet Transit Database indicated the possible existence of other planets affecting the orbit of TrES-1 and improved the precision of the ephemeris by one order of magnitude. We now estimate the ephemeris to be (2 455 489.66026 BJD_TDB ± 0.00044 d) + (3.0300689 ± 0.0000007) d × epoch. This analysis also demonstrates the role of small telescopes in making precise midtransit time measurements, which can be used to help maintain ephemerides and perform TTV analysis. The maintenance of ephemerides allows for an increased ability to optimize telescope time on large ground-based telescopes and space telescope missions.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.51-56
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• 2023

This study investigates the effect of sEMG sampling frequency on CNN learning performance at Korean finger number recognition application. Since the bigger sampling frequency of sEMG signals generates bigger size of input data and takes longer CNN's learning time. It makes making real-time system implementation more difficult and more costly. Thus, there might be appropriate sampling frequency when collecting sEMG signals. To this end, this work choose five different sampling frequencies which are 1,024Hz, 512Hz, 256Hz, 128Hz and 64Hz and investigates CNN learning performance with sEMG data taken at each sampling frequency. The comparative study shows that all CNN recognized Korean finger number one to five at the accuracy of 100% and CNN with sEMG signals collected at 256Hz sampling frequency takes the shortest learning time to reach the epoch at which korean finger number gestures are recognized at the accuracy of 100%.

• Fashion & Textile Research Journal
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• v.9 no.1
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• pp.15-23
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• 2007

In the Middle Ages it was customary to cover up the hair, but the Renaissance brought uncovered coiffures with the revival of humanism. In those days, silk and linen veil, ribbon, string of pearl used for covering, wrapping round with the hair. During the Baroque period, the style of hair was to pursue the beauty of imbalance in form, reflecting the atmosphere of the time. Hurluberlu and Fontanges hairstyles were in fashion. Then in the Rococo period, huge, resplendent coiffures of exquisite beauty were invented as a symbol of power, and these modes of hairdo were a dominant force in the culture of personal adornment of that time. Pouf and enfant hairstyles were in fashion. As a reaction against the extravagance of the proceding modes, late 18th and early 19th centuries brought revival of simpler hairstyles of ancient Greece and Rome by the influence of neoclassicism. The latter half of the 1820's onwards saw he reappearance of voluminous coiffures as well as an enormous variation of knots with combinations of false knots and chignons. Late 19th through early 20th centuries was the period of beautifully waved hair, the style of which was an integration of Marcel waves and Art Nouveau. The 20th century saw the epoch-making invention of permanent waves using electricity. Concurrently, with an increasing participation of women in social affairs since pre-and post-World War I periods, as well as with Art Deco in full flourish, bobbed hair was created in pursuit of lightness and nimbleness, quickly showing the change of women's modes of life. Hair fashions thoroughly embody the aesthetic sense of each period, reflecting the landscape of contemporary society.

• Aerospace Engineering and Technology
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• v.8 no.2
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• pp.61-67
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• 2009

This paper proposes an on-board orbit data generation algorithm for geostationary satellites. The concept of the proposed algorithm is as follows. From the ground, the position and velocity deviations with respect to the assumed reference orbit are computed for 48 hours of time duration in 30 minutes interval, and the generated data are up-loaded to the satellite to be stored. From the table, three nearest data sets are selected to compute position and velocity deviation for asked epoch time by applying $2^{nd}$ order polynomial interpolation. The computed position and velocity deviation data are added to reference orbit to recover absolute orbit information. Here, the reference orbit is selected to be ideal geostationary orbit with a zero inclination and zero eccentricity. Thanks to very low computational burden, this algorithm allows us to generate orbit data at 1Hz or even higher. In order to support 48 hours autonomy, maximum 3K byte memory is required as orbit data storage. It is estimated that this additional memory requirement is acceptable for geostationary satellite application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1387-1393
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• 2007

Even if the linearly separable patterns can be classified by the conventional single layer perceptron, the non-linear problems such as XOR can not be classified by it. A fuzzy single layer perceptron can solve the conventional XOR problems by applying fuzzy membership functions. However, in the fuzzy single layer perception, there are a couple disadvantages which are a decision boundary is sometimes vibrating and a convergence may be extremely lowered according to the scopes of the initial values and learning rates. In this paper, for these reasons, we proposed an enhanced fuzzy single layer perceptron algorithm that can prevent from vibration the decision boundary by introducing a bias term and can also reduce the learn time by applying the modified delta rule which include the learning rates and the momentum concept and applying the new linear activation function. Consequently, the simulation results of the XOR and pattern classification problems presented that the proposed method provided the shorter learning time and better convergence than the conventional fuzzy single layer perceptron.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.52.1-52.1
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• 2021

We present the results of multi-epoch, multi-frequency monitoring of a blazar 4C +29.45, which was regularly monitored as part of the Interferometric Monitoring of GAmma-ray Bright AGNs program - a key science program of the Korean Very long baseline interferometry Network (KVN). Observations were conducted simultaneously at 22, 43, 86 and 129 GHz during the 4 years from December 2012 to December 2016. We also used additional data from the 15 GHz Owens Valley Radio Observatory (OVRO) monitoring program. From the 15 GHz light curve, we estimated the variability time scales of the source during several radio flux enhancements. We found that the source experiencesd 6 radio flux enhancements with variability time scales of 9-187 days during the observing period, yielding corresponding variability Doppler factors of 9-27. From the multi-frequency simultaneous KVN observations, we were able to obtain accurate radio spectra of the source and hence to more precisely measure the turnover frequencies 𝜈_r of synchrotron self-absorbed (SSA) emission with a mean value of ${\bar{\nu}_r}=28.9GHz$. Using jet geometry assumptions, we estimated the size of the emitting region at the turnover frequency. Taking into account these results, we found that the equipartition magnetic field strength is up to two orders of magnitudes higher than the SSA magnetic field strength (0.6-99 mG). This is consistent with the source being particle dominated.