• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.661-667
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• 2006

In this study, an experiment of sewage sludge dewatering is carried by using electrokinetic method, and the electrokinetic dewatering efficiency of digested sludge is analyzed. Digested sludge without coagulants is selected and gravitational and pressing dewatering methods are applied in combination with electro-osmotic and electro-osmotic pulse technology. After the test of digested sludge, dewatering test of thickened sludge is carried to evaluate the electrokinetic dewatering feasibility of thickened sludge. Under the condition of constantly applied voltage, however, electrical resistance increases with decreasing of water content so that dewatering rate decreases with time. To reduce such a hindrance caused by constantly applied voltage, electro-osmotic pulse technology which is considered to reduce the difference of water content with height, is applied. For the application of electro-osmotic pulse, the dewatered flow rate and the dewatered volume became more increasing from the middle of the dewatering process than that of continuous voltage. Through the test of thickened sludge, electro-osmotic dewatering combined with gravitational and expression also showed high dewatering rate, which proved the possibility of using electrokinetic dewatering.

• Steel and Composite Structures
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• v.49 no.1
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• pp.81-89
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• 2023

A simplified calculation method of natural vibration characteristics of high-speed railway multi-span bridge-longitudinal ballastless track system is proposed. The rail, track slab, base slab, main beam, bearing, pier, cap and pile foundation are taken into account, and the multi-span longitudinal ballastless track-beam-bearing-pier-cap-pile foundation integrated model (MBTIM) is established. The energy equation of each component of the MBTIM based on Timoshenko beam theory is constructed. Using the improved Fourier series, and the Rayleigh-Ritz method and Hamilton principle are combined to obtain the extremum of the total energy function. The simplified calculation formula of the natural vibration frequency of the MBTIM under the influence of vertical and longitudinal vibration is derived and verified by numerical methods. The influence law of the natural vibration frequency of the MBTIM is analyzed considering and not considering the participation of each component of the MBTIM, the damage of the track interlayer component and the stiffness change of each layer component. The results show that the error between the calculation results of the formula and the numerical method in this paper is less than 3%, which verifies the correctness of the method in this paper. The high-order frequency of the MBTIM is significantly affected considering the track, bridge pier, pile soil and pile cap, while considering the influence of pile cap on the low-order and high-order frequency of the MBTIM is large. The influence of component damage such as void beneath slab, mortar debonding and fastener failure on each order frequency of the MBTIM is basically the same, and the influence of component damage less than 10m on the first fourteen order frequency of the MBTIM is small. The bending stiffness of track slab and rail has no obvious influence on the natural frequency of the MBTIM, and the bending stiffness of main beam has influence on the natural frequency of the MBTIM. The bending stiffness of pier and base slab only has obvious influence on the high-order frequency of the MBTIM. The natural vibration characteristics of the MBTIM play an important guiding role in the safety analysis of high-speed train running, the damage detection of track-bridge structure and the seismic design of railway bridge.

• Resources Recycling
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• v.32 no.6
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• pp.54-66
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• 2023

Titanium's importance as a mineral resource is increasing, but the Korean industry depends on imports. Ilmenite is the principal titanium ore. However, research and development from raw materials have not been investigated yet in detail. Hence, measures to secure a stable titanium supply chain are urgently needed. Accordingly, through beneficiation technology, we evaluated the possibility of technological application for the efficient recovery of valuable minerals. As a result of the experiments, we confirmed that mineral particles existed as fine particles due to weathering, making recovery through classification difficult. Consequently, applying beneficiation technologies, i.e., specific gravity separation, magnetic separation, and flotation, makes it possible to recover valuable minerals such as hematite and rutile. However, there are limitations in increasing the quality and yield of TiO₂ due to the mineralogical characteristic of the hematite and rutile contained in titanium ore. Hametite is combined with rutile even at fine particles. Therefore, it is essential to develop mineral processing routes, to recover iron, vanadium, and rare earth elements as resources. On that account, we used grinding technology that improves group separation between constituent minerals and magnetic separation technology that utilizes the difference in magnetic sensitivity between fine mineral particles. The development of beneficiation technology that can secure the economic feasibility of valuable materials after reforming iron oxide and titanium oxide components is necessary.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1111-1123
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• 2023

In recent times, semantic image segmentation methods using deep learning models have been widely used for monitoring changes in surface attributes using remote sensing imagery. To enhance the performance of various UNet-based deep learning models, including the prominent UNet model, it is imperative to have a sufficiently large training dataset. However, enlarging the training dataset not only escalates the hardware requirements for processing but also significantly increases the time required for training. To address these issues, transfer learning is used as an effective approach, enabling performance improvement of models even in the absence of massive training datasets. In this paper we present three transfer learning models, UNet-ResNet50, UNet-VGG19, and CBAM-DRUNet-VGG19, which are combined with the representative pretrained models of VGG19 model and ResNet50 model. We applied these models to building extraction tasks and analyzed the accuracy improvements resulting from the application of transfer learning. Considering the substantial impact of learning rate on the performance of deep learning models, we also analyzed performance variations of each model based on different learning rate settings. We employed three datasets, namely Kompsat-3A dataset, WHU dataset, and INRIA dataset for evaluating the performance of building extraction results. The average accuracy improvements for the three dataset types, in comparison to the UNet model, were 5.1% for the UNet-ResNet50 model, while both UNet-VGG19 and CBAM-DRUNet-VGG19 models achieved a 7.2% improvement.

• Korean Journal of Environmental Agriculture
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• v.42 no.4
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• pp.331-337
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• 2023

Physiological disorders in pear fruit are mainly caused by problems during the growing season, such as lack of calcium in the soil, poor drainage, low porosity, vigorous pruning, and excessive fruiting. In this study, soil physicochemical properties and leaf characteristics were analyzed in pear orchards in four regions of Korea where chlorosis symptoms occurred to determine the causes of chlorosis. The color of chlorotic leaves was diagnosed using the naked eye or SPAD and Hunter values. The soil of the chlorotic orchard had a significantly higher soil pH than that of the regular orchard. Although adequate soil depth was not significantly associated with chlorosis, combined with over-fertilization of the soil with lime, it could potentially impair plant iron uptake. Chlorotic leaves had significantly lower iron and calcium contents and significantly higher magnesium contents than those of regular leaves. Therefore, the intensive occurrence of chlorosis during secondary shoot development around June and July when it is hot and humid may be due to impaired iron and calcium absorption, leading to physiological disorders. To solve this problem, avoiding the over-application of lime and applying foliar fertilizers containing chelated iron is recommended.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.218-225
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• 2023

An agrivoltaic system (AVS) is a combined system that generates power through photovoltaic panels (PVPs) installed above a field where a crop is cultivated. Although soil moisture is an important limiting factor for open-field crop production, particularly during spring season in Korea, it is not well considered in the utilization of AVS. Indeed, the application of water-energy-food nexus on the AVS should be necessary. In this study, the changes of soil moisture and temperature under the AVS was investigated in fallow paddy field during spring season. The AVS that has partial shading condition by PV panels was decreased soil temperature and increased soil moisture compared to open-field. Furthermore, the maximum of the change in soil moisture to the change in soil temperature had a negative correlation both on open-field and AVS under wet condition. It represents that the micro-climate under the AVS is in energy-limited condition. The open-field of relatively high soil temperature was in water-limited condition. The different behavior of soil moisture on the AVS should be considered for the sustainable agricultural system as related to water-energy-food nexus.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.994-1004
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• 2021

Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

• Korean Journal of Radiology
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• v.23 no.8
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• pp.811-820
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• 2022

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.321-332
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• 2024

Understanding the status of surface cover in riparian zones is essential for river management and flood disaster prevention. Traditional survey methods rely on expert interpretation of vegetation through vegetation mapping or indices. However, these methods are limited by their ability to accurately reflect dynamically changing river environments. Against this backdrop, this study utilized satellite imagery to apply the Random Forest method to assess the distribution of vegetation in rivers over multiple years, focusing on the Naeseong Stream as a case study. Remote sensing data from Sentinel-2 imagery were combined with ground truth data from the Naeseong Stream surface cover in 2016. The Random Forest machine learning algorithm was used to extract and train 1,000 samples per surface cover from ten predetermined sampling areas, followed by validation. A sensitivity analysis, annual surface cover analysis, and accuracy assessment were conducted to evaluate their applicability. The results showed an accuracy of 85.1% based on the validation data. Sensitivity analysis indicated the highest efficiency in 30 trees, 800 samples, and the downstream river section. Surface cover analysis accurately reflects the actual river environment. The accuracy analysis identified 14.9% boundary and internal errors, with high accuracy observed in six categories, excluding scattered and herbaceous vegetation. Although this study focused on a single river, applying the surface cover classification method to multiple rivers is necessary to obtain more accurate and comprehensive data.

• Tuberculosis and Respiratory Diseases
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• v.44 no.1
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• pp.52-58
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• 1997

Background : Tuberculous pleurisy has been treated With a combined regimen of corticosteroids- antimicrobial therapy. But whether such combination therapy add to benefits is unknown. We evaluate the effects of corticosteroid and its routine application in relief of clinical symptoms, absorption of pleural effusion, and pleural adhesions. Methods : A prospective, randomized study of the role of corticosteroid in the treatment of tuberculous pleurisy was performed in 83 patients(nonsteroid group: 50 patients, steroid group: 33 patients) from June, 1991 to September, 1994. Results : 1) The mean duration from symptoms(fever, chest pain, dyspnea) to relief was 3.8 days in the steroid group, and 7.4 days in the nonsteroid group(P<0.05). Clinical symptoms including fever, chest pain, sputum and weight loss were relieved more rapidly in the steroid group than other symptoms(weakness, night sweating and dyspnea). 2) Pleural effusion was taken an averge of 88 days in the steroid group and 101 days in the nonsteroid group 10 be absorbed completely(p>0.05) 3) The incidence of pleural adhesions was 17/33(5l.5%) in the steroid group and 32/50(64%) in the nonsteroid group(p>0.05) 4) Side effects of corticosteroids were observed in only one patient causing epigastric pain and discontinuation of drug. Conclusion : Corticosteroid exert benefitial role in the more rapid relief of clinical symptoms to patients with tuberculous pleurisy, but absortion of pleural effusion and occurrence of pleural adhesions was not influenced significantly Therefore, its routine application should be reevaluated.