• Journal of the Korea institute for structural maintenance and inspection
• /
• v.17 no.6
• /
• pp.50-60
• /
• 2013

Dynamic response of steel plate girder bridges by KL-510 design truck in KHBDC considering the road surface roughness of bridges and bridge-vehicle interaction is investigated. Simply supported steel plate girder bridges with span length of 20m, 30m, and 40m from "Standard Highway Bridge Superstructure" published by the Korean Ministry of Construction are used for a bridge model, and ten sets of the road surface roughness of bridge deck are generated from power spectral density (PSD) function by assuming the roadway as "Average Road". A three dimensionally modeled 5-axle tractor-trailer with its gross weight, which is the same as that of KL-510 design truck, is used for dynamic analysis. For the finite element modeling of superstructure, beam element for the main girder, shell element for the concrete deck, and rigid link between main girder and concrete deck are used. Impact factor and DLA of steel plate girder bridges for different span are calculated by the proposed numerical analysis model and compared with those specified by several bridge codes.

• Korean Journal of Remote Sensing
• /
• v.15 no.4
• /
• pp.357-365
• /
• 1999

The optical sensors of Electro-Optical Camera (EOC) and Ocean Scanning Multi-spectral Imager (OSMI) aboard the KOrea Multi-Purpose SATellite (KOMPSAT) will be placed in a sun synchronous orbit in late 1999. The EOC and OSMI sensors are expected to produce the land mapping imagery of Korean territory and the ocean color imagery of world oceans, respectively. Utilization of the EOC and OSMI data would encompass the various fields of science and technology such as land mapping, land use and development, flood monitoring, biological oceanography, fishery, and environmental monitoring. Readiness of data support for user community is thus essential to the success of the KOMPSAT program. As a part of testing such readiness prior to the KOMPSAT launch, we have performed the preliminary acceptance test for the KOMPSAT data processing system using the simulated EOC and OSMI data sets. The purpose of this paper is to demonstrate the readiness of the KOMPSAT data processing system, and to help data users understand how the KOMPSAT EOC and OSMI data are processed, archived, and provided. Test results demonstrate that all requirements described in the data processing specification have been met, and that the image integrity is maintained for all products. It is however noted that since the product accuracy is limited by the simulated sensor data, any quantitative assessment of image products can not be made until actual KOMPSAT images will be acquired.

• The Bulletin of The Korean Astronomical Society
• /
• v.44 no.1
• /
• pp.42.2-42.2
• /
• 2019

Turbulence is one of the natural phenomena in molecular clouds. It affects gas density and velocity fluctuation within the molecular clouds and controls the mode and tempo of star formation. However, despite many years of study, the properties of turbulence remain poorly understood. As part of the Taeduk Radio Astronomy Observatory (TRAO) Key Science Program (KSP), "mapping Turbulent properties In star-forming MolEcular clouds down to the Sonic scale (TIMES; PI: Jeong-Eun Lee)", we have fully mapped two star-forming molecular clouds, the Orion A and the Ophiuchus molecular clouds, in 3 sets of lines ($^{13}CO$ J=1-0, $C^{18}O$ J=1-0, HCN J=1-0, $HCO^+$ J=1-0, CS J=2-1, and $N_2H^+$ J=1-0) using the TRAO 14-m telescope. We apply a statistical analysis, Principal Component Analysis (PCA), which can recover an underlying turbulent-power spectrum from an observed P-P-V spectral map. We compare turbulence properties not only between the two clouds, but also between different parts within each cloud. We present the first result of our observation program.

• Current Optics and Photonics
• /
• v.5 no.3
• /
• pp.270-277
• /
• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• Geophysics and Geophysical Exploration
• /
• v.24 no.4
• /
• pp.171-179
• /
• 2021

We outline a process for estimating Cole-Cole parameters from time-domain induced polarization (IP) data. The IP transients are all inverted to 2D Cole-Cole earth models that include resistivity, chargeability, relaxation time, and the frequency exponent. Our inversion algorithm consists of two stages. We first convert the measured voltage decay curves into time series of current-on time apparent resistivity to circumvent the negative chargeability problem. As a first step, a 4D inversion recovers the resistivity model at each time channel that increases monotonically with time. The desired intrinsic Cole-Cole parameters are then recovered by inverting the resistivity time series of each inversion block. In the second step, the Cole-Cole parameters can be estimated readily by setting the initial model close to the true value through a grid search method. Finally, through inversion procedures applied to synthetic data sets, we demonstrate that our algorithm can image the Cole-Cole earth models effectively.

• Journal of Cadastre & Land InformatiX
• /
• v.53 no.1
• /
• pp.107-121
• /
• 2023

In order to acquire a wide range of land that changes in real time and quickly and accurately grasp it, we plan to utilize the recently released high-resolution S.Korea's satellite image data and artificial intelligence (AI). Compared to existing satellite images, the spectral and periodic resolutions of S.Korea's satellite are higher, making them a more suitable data source for periodically monitoring changes in land. Therefore, this study aims to acquire S.Korea's satellite, select 8 types of objects to detect land changes, construct data sets for them, and apply AI models to analyze them. In order to confirm the optimal model and variable conditions for detecting 8 types of objects of various types, several experiments are performed and AI-based image analysis is technically reviewed.

• The Bulletin of The Korean Astronomical Society
• /
• v.44 no.2
• /
• pp.68.1-68.1
• /
• 2019

Turbulence plays a crucial role in controlling star formation as it produces density fluctuation as well as non-thermal pressure against gravity. Therefore, turbulence controls the mode and tempo of star formation. However, despite a plenty of previous studies, the properties of turbulence remain poorly understood. As part of the Taeduk Radio Astronomy Observatory (TRAO) Key Science Program (KSP), "mapping Turbulent properties In star-forming MolEcular clouds down to the Sonic scale (TIMES; PI: Jeong-Eun Lee)", we mapped the Orion A and the Ophiuchus clouds, in three sets of lines (13CO 1-0/C18O 1-0, HCN 1-0/HCO+ 1-0, and CS 2-1/N2H+ 1-0) with a high-velocity resolution (~0.1 km/s) using the TRAO 14-m telescope. The mean Trms for the observed maps are less than 0.25 K, and all these maps show uniform Trms values throughout the observed area. These homogeneous and high signal-to-noise ratio data provide the best chance to probe the nature of turbulence in two different star-forming clouds, the Orion A and Ophiuchus clouds. We present comparisons between the line intensities of different molecular tracers as well as the results of a Principal Component Analysis (PCA).

• Journal of the Earthquake Engineering Society of Korea
• /
• v.27 no.6
• /
• pp.245-252
• /
• 2023

In general, the design response spectrum in seismic design codes is based on the mean-plus-one-standard deviation response spectrum to secure high safety. In this study, response spectrum analysis was performed using seismic wave records adopted in domestic horizontal design spectrum development studies, while three response spectra were calculated by combining the mean and standard deviation of the spectra. Seismic wave spectral matching generated seismic wave sets matching each response spectrum. Then, seismic fragility was performed by setting three damage levels using a single-degree-of-freedom system. A correlation analysis was performed using a comparative analysis of the change in the response spectrum and the seismic fragility concerning the three response spectra. Finally, in the case of the response spectrum considering the mean and standard deviation, like the design response spectrum, the earthquake load was relatively high, indicating that conservative design or high safety can be secured.

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

• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.449-461
• /
• 2021

Analysis Ready Data (ARD) for optical satellite images represents a pre-processed product by applying spectral characteristics and viewing parameters for each sensor. The atmospheric correction is one of the fundamental and complicated topics, which helps to produce Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance from multi-spectral image sets. Most remote sensing software provides algorithms or processing schemes dedicated to those corrections of the Landsat-8 OLI sensors. Furthermore, Google Earth Engine (GEE), provides direct access to Landsat reflectance products, USGS-based ARD (USGS-ARD), on the cloud environment. We implemented the Orfeo ToolBox (OTB) atmospheric correction extension, an open-source remote sensing software for manipulating and analyzing high-resolution satellite images. This is the first tool because OTB has not provided calibration modules for any Landsat sensors. Using this extension software, we conducted the absolute atmospheric correction on the Landsat-8 OLI images of Railroad Valley, United States (RVUS) to validate their reflectance products using reflectance data sets of RVUS in the RadCalNet portal. The results showed that the reflectance products using the OTB extension for Landsat revealed a difference by less than 5% compared to RadCalNet RVUS data. In addition, we performed a comparative analysis with reflectance products obtained from other open-source tools such as a QGIS semi-automatic classification plugin and SAGA, besides USGS-ARD products. The reflectance products by the OTB extension showed a high consistency to those of USGS-ARD within the acceptable level in the measurement data range of the RadCalNet RVUS, compared to those of the other two open-source tools. In this study, the verification of the atmospheric calibration processor in OTB extension was carried out, and it proved the application possibility for other satellite sensors in the Compact Advanced Satellite (CAS)-500 or new optical satellites.