• Korean Journal of Remote Sensing
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• v.13 no.2
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• pp.85-98
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• 1997

A geometric model of pushbroom-type linear CCD camera images is proposed in this paper. At present, this type of cameras are used for obtaining almost all kinds of high-resolution optical images from satellites. The proposed geometric model includes not only a forward transformation which is much more efficient. An inverse transformation function cannot be derived analytically in a closed form because the focal point of an image varies with time. In this paper, therefore, an iterative algorithm in which a focal point os converged to a given pixel position is proposed. Although the proposed model can be applied to any pushbroom-type linear CCD camera images, the geometric model of the high-resolution multi-spectral camera on-board KITSAT-3 is used in this paper as an example. The flight model of KITSAT-3 is in development currently and it is due to be launched late 1998.

• Journal of the Korean Solar Energy Society
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• v.35 no.1
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• pp.9-19
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• 2015

Pyranometer have many uncertainty factors (sensitivity function, thermal offset, other spectral effect, geometric, environment, and equipment etc.) than pyrheliometer. The solution for most of the uncertainty factors have been researched, but the problem for thermal offset is being continued research so far. Under the clear sky, due to the thermal offset of pyranometer, the diffuse and global radiation have been negative value for the nighttime and lower value for the daytime, respectively. In order to understand the uncertainty of the thermal offset effect, solar radiation are observed and analyzed using Ji and Tsay method and data from modified pyranometer. As a result of performing temperature correction using the modified pyranometer, the slope (dome factor; k) and intercept ($r_0$) from a linear regression method are 0.064 and $3.457g{\cdot}m^{-2}{\cdot}k^{-1}$, respectively. And the solar radiation is decreased significantly due to the effect of thermal offset during nighttime. The solar radiation from modified pyranometer increased approximately 8% higher than its observed by general pyranometer during daytime. By the way, these results did not generalize because its result is for only single case in clear sky. Accordingly, it is to required for accurate results obtained by the various cases (clear, cloudy and rainy) with longterm observations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.326-332
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• 2019

Recently, the deep learning technology has gained lots of attention which leads to its application to various fields. Especially, there are recent attempts to overcome the limit of wireless communications systems through the use of the deep learning. In this paper, we have verified the performance of deep learning based transmit power control scheme. Unlike previous transmit power control schemes where the optimal transmit power is derived by solving the optimization problem explicitly, in the deep learning based transmit power control, the general solver for the optimization problem is derived through the deep neural network (DNN). Especially, by using the spectral efficiency as the loss function of DNN, the training can be performed without needing labels. Through simulation based on Tensorflow, we confirm that the transmit power control based on deep learning can achieve the optimal performance while reducing the computational complexity by 1/200.

• Earthquakes and Structures
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• v.16 no.5
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• pp.563-573
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• 2019

When generating spectrum-compatible artificial ground motion in engineering practices, the effect of the variation in fitting parameters on the distribution of the peak ground displacement (PGD) has not yet drawn enough attention. In this study, a method for simulating ground motion matching for multiple targets is developed. In this method, a frequency-dependent amplitude envelope function with statistical parameters is introduced to simulate the nonstationarity of the frequency in earthquake ground motion. Then, several groups of time-history acceleration with different temporal and spectral nonstationarities were generated to analyze the effect of nonstationary parameter variations on the distribution of PGD. The following conclusions are drawn from the results: (1) In the simulation of spectrum-compatible artificial ground motion, if the acceleration time-history is generated with random initial phases, the corresponding PGD distribution is quite discrete and an uncertain number of PGD values lower than the limit value are observed. Nevertheless, the mean values of PGD always meet the requirement in every group. (2) If the nonstationary frequencies of the ground motion are taken into account when fitting the target spectrum, the corresponding PGD values will increase. A correlation analysis shows that the change in the mean and the dispersion values, from before the frequencies are controlled to after, correlates with the modal parameters of the predominant frequencies. (3) Extending the maximum period of the target spectrum will increase the corresponding PGD value and, simultaneously, decrease the PGD dispersion. Finally, in order to control the PGD effectively, the ground motion simulation method suggested in this study was revised to target a specified PGD. This novel method can generate ground motion that satisfies not only the required precision of the target spectrum, peak ground acceleration (PGA), and nonstationarity characteristics of the ground motion but also meets the required limit of the PGD, improving engineering practices.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.343-353
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• 2022

An attempt to derive the surface temperature from the Korea Multi-purpose Satellite (KOMPSAT)-3A mid-wave infrared (MWIR) data acquired over the southern California on Nov. 14, 2015 has been made using the MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer model. Since after the successful launch on March 25, 2015, the KOMPSAT-3A spacecraft and its two payload instruments - the high-resolution multispectral optical sensor and the scanner infrared imaging system (SIIS) - continue to operate properly. SIIS uses the MWIR spectral band of 3.3-5.2 ㎛ for data acquisition. As input data for the realistic simulation of the KOMPSAT-3A SIIS imaging conditions in the MODTRAN model, we used the National Centers for Environmental Prediction (NCEP) atmospheric profiles, the KOMPSAT-3Asensor response function, the solar and line-of-sight geometry, and the University of Wisconsin emissivity database. The land cover type of the study area includes water,sand, and agricultural (vegetated) land located in the southern California. Results of surface temperature showed the reasonable geographical pattern over water, sand, and agricultural land. It is however worthwhile to note that the surface temperature pattern does not resemble the top-of-atmosphere (TOA) radiance counterpart. This is because MWIR TOA radiances consist of both shortwave (0.2-5 ㎛) and longwave (5-50 ㎛) components and the surface temperature depends solely upon the surface emitted radiance of longwave components. We found in our case that the shortwave surface reflection primarily causes the difference of geographical pattern between surface temperature and TOA radiance. Validation of the surface temperature for this study is practically difficult to perform due to the lack of ground truth data. We therefore made simple comparisons with two datasets over Salton Sea: National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) field data and Salton Sea data. The current estimate differs with these datasets by 2.2 K and 1.4 K, respectively, though it seems not possible to quantify factors causing such differences.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.50.2-51
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• 2021

Stars form exclusively in cold and dense molecular clouds. To fully understand star formation processes, it is hence a key to investigate how molecular clouds form out of the surrounding diffuse atomic gas. With an aim of shedding light in the process of the atomic-to-molecular transition in the interstellar medium, we analyze Arecibo HI emission and absorption spectral pairs along with TRAO/PMO 12CO(1-0) emission spectra toward 58 lines of sight probing in and around molecular clouds in the solar neighborhood, i.e., Perseus, Taurus, and California. 12CO(1-0) is detected from 19 out of 58 lines of sight, and we report the physical properties of HI (e.g., central velocity, spin temperature, and column density) in the vicinity of CO. Our preliminary results show that the velocity difference between the cold HI (Cold Neutral Medium or CNM) and CO (median ~ 0.7 km/s) is on average more than a factor of two smaller than the velocity difference between the warm HI (Warm Neutral Medium or WNM) and CO (median ~ 1.7 km/s). In addition, we find that the CNM tends to become colder (median spin temperature ~ 43 K) and abundant (median CNM fraction ~ 0.55) as it gets closer to CO. These results hints at the evolution of the CNM in the vicinity of CO, implying a close association between the CNM and molecular gas. Finally, in order to examine the role of HI in the formation of molecular gas, we compare the observed CNM properties to the theoretical model by Bialy & Sternberg (2016), where the HI column density for the HI-to-H2 transition point is predicted as a function of density, metallicity, and UV radiation field. Our comparison shows that while the model reproduces the observations reasonably well on average, the observed CNM components with high column densities are much denser than the model prediction. Several sources of this discrepancy, e.g., missing physical and chemical ingredients in the model such as the multi-phase ISM, non-equilibrium chemistry, and turbulence, will be discussed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1A
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• pp.39-49
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• 2008

Dynamic responses of steel plate girder bridges considering road surface roughness of bridge and bridge-vehicle interaction are investigated by numerical analysis. Simply supported steel plate girder bridges with span length of 20 m, 30 m, and 40 m from "The Standardized Design of Highway Bridge Superstructure" published by the Korean Ministry of Construction are used for bridge model and the road surface roughness of bridge decks are generated from power spectral density(PSD) function for different road. Three different vehicles of 2- and 3-axle dump trucks, and 5-axle tractor-trailer(DB-24), are modeled three dimensionally. For the bridge superstructure, beam elements for the main girder, shell elements for concrete deck, and rigid links between main girder and concrete deck are used. Impact factor and DLA of steel plate girder bridges for different spans, type of vehicles and road surface roughnesses are calculated by the proposed numerical analysis model and compared with those specified by several bridge design codes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6A
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• pp.989-999
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• 2006

In this paper, numerical analysis method to perform linear dynamic analysis of bridge considering the road surface roughness and bridge-vehicle interaction when vehicle is moving on bridge is presented. The vehicle and bridge are modeled as three-dimension where contact length of tire and pitching of tandem spring are considered and single truck with 2-axles and 3- axles, and tractor-trailer with 5-axles are modeled as 7-D.O.F., 8-D.O.F., and 14-D.O.F., respectively. Dynamic equations of vehicle are derived from the Lagrange's equation and solution of the equation is obtained by Newmark-${\beta}$ method. The surface roughness of bridge deck for this analysis is generated from power spectral density (PSD) function. Beam element for the main girder, shell element for concrete deck and rigid link between main girder and concrete deck are used. The equations of the motion of bridges are solved by mode-superposition procedures. The proposed procedure is validated by comparing the results with the experimental data by Whittemore and Fenves.

• Current Optics and Photonics
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• v.8 no.1
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• pp.38-44
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• 2024

Under the influence of atmospheric turbulence, a star's point image will shake back and forth erratically, and after exposure the originally small star point will spread into a huge spot, which will affect the ground calibration of the star sensor. To analyze the impact of atmospheric turbulence on the positioning accuracy of the star's center of mass, this paper simulates the atmospheric turbulence phase screen using a method based on a sparse spectrum. It is added to the static-star-simulation device to study the transmission characteristics of atmospheric turbulence in star-point simulation, and to analyze the changes in star points under different atmospheric refractive-index structural constants. The simulation results show that the structure function of the atmospheric turbulence phase screen simulated by the sparse spectral method has an average error of 6.8% compared to the theoretical value, while the classical Fourier-transform method can have an error of up to 23% at low frequencies. By including a simulation in which the phase screen would cause errors in the center-of-mass position of the star point, 100 consecutive images are selected and the average drift variance is obtained for each turbulence scenario; The stronger the turbulence, the larger the drift variance. This study can provide a basis for subsequent improvement of the ground-calibration accuracy of a star sensitizer, and for analyzing and evaluating the effect of atmospheric turbulence on the beam.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.