• Smart Structures and Systems
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• v.25 no.3
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• pp.369-384
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• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.251-258
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• 2021

In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analysis. The conventional U-Net occurs loss in feature compression process due to the shallow structure of the encoder. The loss of features causes a lack of context information necessary for classifying objects and has a problem of reducing segmentation accuracy. To improve this, The proposed method efficiently extracted context information through an encoder using residual learning, which is effective in preventing feature loss and gradient vanishing problems in the conventional U-Net. Furthermore, we reduced down-sampling operations in the encoder to reduce the loss of spatial information included in the feature maps. The proposed method showed an improved segmentation result of about 12% compared to the conventional U-Net in the Cityscapes dataset experiment.

• Geomechanics and Engineering
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• v.27 no.5
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• pp.433-445
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• 2021

Artificial ground freezing (AGF) is a commonly used geotechnical support technique that can be applied in any soil type and has low environmental impact. Experimental and numerical investigations have been conducted to optimize AGF for application in diverse scenarios. Precise simulation of groundwater flow is crucial to improving the reliability these investigations' results. Previous experimental research has mostly considered horizontal seepage flow, which does not allow accurate calculation of the groundwater flow velocity due to spatial variation of the piezometric head. This study adopted vertical seepage flow-which can maintain a constant cross-sectional area-to eliminate the limitations of using horizontal seepage flow. The closure time is a measure of the time taken for an impermeable layer to begin to form, this being the time for a frozen soil-ice wall to start forming adjacent to the freeze pipes; this is of great importance to applied AGF. This study reports verification of the reliability of our experimental apparatus and measurement system using only water, because temperature data could be measured while freezing was observed visually. Subsequent experimental AFG tests with saturated sandy soil were also performed. From the experimental results, a method of estimating closure time is proposed using the inflection point in the thermal conductivity difference between pore water and pore ice. It is expected that this estimation method will be highly applicable in the field. A further parametric study assessed factors influencing the closure time using a two-dimensional coupled thermo-hydraulic numerical analysis model that can simulate the AGF of saturated sandy soil considering groundwater flow. It shows that the closure time is affected by factors such as hydraulic gradient, unfrozen permeability, particle thermal conductivity, and freezing temperature. Among these factors, changes in the unfrozen permeability and particle thermal conductivity have less effect on the formation of frozen soil-ice walls when the freezing temperature is sufficiently low.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4D
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• pp.443-450
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• 2009

Uninterrupted facility - since there is a close relationship between traffic volume, speed and density -, when a ramp traffic flow merges into the main line, will change the traffic speed or density, and the corresponding correlational model equation will be changed. Thus, this study, using time and space-series traffic data on areas under the influence of such a merging, identified sections which changed the correlation between speed and density variables, and examined such changes. As a result, the upstream and merging sections showed the "Underwood"-shaped exponent, and the downstream after passing the merging section showed a straight line "Greenshields" model. The downstream section which changed the correlation between speed and density showed a gradual downstream movement phenomenon within 100 m-500 m from the end of the third lane linking with the ramp, as the traffic approached the inner lanes. Also, the upstream section, merging section, and downstream section involving a change showed heterogeneous traffic flows which, in the speed-density model, have a statistically different free flow speed (constant) and a different ratio of free flow speed to jam density (gradient).

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.516-516
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• 2022

Agricultural reservoirs are critical water resources structures to ensure continuous water supplies for rice cultivation in Korea. Climate change has increased the risk of reservoir failure by exacerbating discrepancies in upstream runoff generation, downstream irrigation water demands, and evaporation losses. In this study, the variations in water balance components of 400 major reservoirs during 1973-2017 were examined to identify the reservoirs with reliable storage capacities and resilience. A conceptual lumped hydrological model was used to transform the incident rainfall into the inflows entering the reservoirs and the paddy water balance model was used to estimate the irrigation water demand. Historical climate data analysis showed a sharp warming gradient during the last 45 years that was particularly evident in the central and southern regions of the country, which were also the main agricultural areas with high reservoir density. We noted a country-wide progressive increase in average annual cumulative rainfall, but the forcing mechanism of the rainfall increment and its spatial-temporal trends were not fully understood. Climate warming resulted in a significant increase in irrigation water demand, while heavy rains increased runoff generation in the reservoir watersheds. Most reservoirs had reliable storage capacities to meet the demands of a 10-year return frequency drought but the resilience of reservoirs gradually declined over time. This suggests that the recovery time of reservoirs from the failure state had increased which also signifies that the duration of the dry season has been prolonged while the wet season has become shorter and/or more intense. The watershed-irrigated area ratio (W-I_ratio) was critical and the results showed that a slight disruption in reservoir water balance under the influence of future climate change would seriously compromise the performance of reservoirs with W-I_ratio< 5.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.151-160
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• 2023

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.

• Korean Journal of Environment and Ecology
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• v.34 no.1
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• pp.63-71
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• 2020

This study characterized the spatial and seasonal patterns of light pollution in the Deogyusan National Park and examined the potential effects of light pollution on ecosystems in the park using light intensities derived from VIIRS (Visible Infrared Imaging Radiometer Suite) DNB (Day and Night Band) nightlight images collected in January and August 2018. Results showed that the Muju Deogyusan resort had the greatest light intensity than other sources of light pollution in the park, and light intensity of the resort was much higher in January than in August, suggesting that artificial lights in ski slopes and facilities were the major source of light pollution in the park. An analysis of an urban-natural light pollution gradient along a neighboring urban area through the inside of the park indicated that light radiated from a light pollution source permeated for up to 1km into the adjacent area and contaminated the edge area of the park. Of the legally protected species whose distributions were reported in literature, four mammals (Martes flavigula, Mustela nivalis, Prionailurus bengalensis, Pteromys volans aluco), two birds (Falco subbuteo, Falco tinnunculus), and nine amphibians and reptiles (Onychodactylus koreanus, Hynobius leechii, Karsenia koreana, Rana dybowskii, Rana huanrenensis, Elaphe dione, Rhabdophis tigrinus, Gloydius ussuriensis, Gloydius saxatilis) inhabited light-polluted areas. Of those species inhabiting light-polluted areas, nocturnal species, such as Prionailurus bengalensis and Pteromys volans aluco, in particular, were vulnerable to light pollution. These results implied that protecting ecosystems from light pollution in national parks requires managing nighttime light in the parks and surrounding areas and making a plan to manage nighttime light pollution by taking into account ecological characteristics of wild animals in the parks.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1285-1300
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• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.