• The Korean Journal of Pain
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• v.36 no.2
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• pp.184-194
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• 2023

Background: Degenerative lumbar spondylolisthesis (DLS) is frequently associated with lumbar spinal stenosis (LSS) and conservative treatments such as epidural steroid injection do not have long-term benefits in LSS patients with DLS. This study evaluated the effectiveness of percutaneous epidural neuroplasty using a balloon catheter in patients with LSS and DLS. Methods: Patients' sex, age, body mass index, diabetes, hypertension, stenosis grading, pain duration, location, pain intensity, and medications were retrieved from electronic medical records. At 1, 3, and 6 months following the procedure, data on pain severity, medication usage, and physical functional status were analyzed. A generalized estimating equations model was used at the six-month follow-up. Patients were divided into those with DLS (the spondylolisthesis group) and those without DLS (the no spondylolisthesis group) to evaluate whether the effects of percutaneous epidural neuroplasty using a balloon catheter were different. Results: A total of 826 patients were included (spondylolisthesis: 433 patients, 52.4%; no spondylolisthesis: 393 patients, 47.6%). Age, body mass index, hypertension, pain location, and stenosis grading were statistically different between the two groups. The generalized estimating equations analyses with unadjusted and adjusted estimation revealed a significant improvement in the estimated mean numerical rating scale of pain intensities compared to that at baseline in both groups (P < 0.001). Any adverse events that occurred were minor and temporary. Conclusions: Percutaneous epidural neuroplasty using a balloon catheter may be an alternative treatment option for patients with chronic LSS, regardless of accompanying DLS, who have had failed conservative management.

• Atmosphere
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• v.32 no.1
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• pp.51-60
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• 2022

Corona Virus Disease 19 pandemic (COVID-19) causes many deaths worldwide, and has enormous impacts on society and economy. The COVID-19 was caused by a new type of coronavirus (Severe Acute Respiratory Syndrome Cornonavirus 2; SARS-CoV-2), which has been found that these viruses can be effectively inactivated by ultraviolet (UV) radiation of 290~315 nm. In this study, 90% inactivation time of the SARS-CoV-2 virus was analyzed using ground observation data from Brewer spectrophotometer at Yonsei University, Seoul and simulation data from UVSPEC for the period of 2015~2017 and 2020. Based on 12:00-13:00 noon time, the shortest virus inactivation time were estimated as 13.5 minutes in June and 4.8 minutes in July/August, respectively, under all sky and clear sky conditions. In the diurnal and seasonal variations, SARS-CoV-2 could be inactivated by 90% when exposed to UV radiation within 60 minutes from 10:00 to 14:00, for the period of spring to autumn. However, in winter season, the natural prevention effect was meaningless because the intensity of UV radiation weakened, and the time required for virus inactivation increased. The spread of infectious diseases such as COVID-19 is related to various and complex interactions of several variables, but the natural inactivation of viruses by UV radiation presented in this study, especially seasonal differences, need to be considered as major variables.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.155-162
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• 2009

Univariate frequency analyses are widely used in practical hydrologic design. However, a storm event is usually characterized by amount, intensity, and duration of the storm. To fully understand these characteristics and to use them appropriately in hydrologic design, a multivariate statistical approach is necessary. This study applied a Gumbel mixed model to a bivariate storm frequency analysis using hourly rainfall data collected for 46 years at the Seoul rainfall gauge station in Korea. This study estimated bivariate return periods of a storm such as joint return periods and conditional return periods based on the estimation of joint cumulative distribution functions of storm characteristics. These information on statistical behaviors of a storm can be of great usefulness in the analysis and assessment of the risk associated with hydrologic design problems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3C
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• pp.167-178
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• 2008

Rainfall-induced slope failures were simulated by seepage and stability analyses for actual slopes of weathered soils. After undisturbed sampling and testing on a specimen of unsaturated conditions, a seepage analysis was performed under actual rainfall and it was found that the pore water pressure increased at the boundary of soil and rock layers. The safety factor of slope stability decreased below 1.0 and the failure of actual slope could be simulated. Under design rainfall intensity, the seepage analysis could not include the effects of the antecedent rainfall and the rainfall duration. Due to these limitations, the safety factor of slope stability resulted in above 1.0, since the hydraulic head of soil layers had not be affected significantly. In the analysis of another slope failure, the parameters of unsaturated conditions were evaluated using artificial neural network (ANN). In the analysis of seepage, the boundary of soil and rock was saturated sufficiently and then the safety factor could be calculated below 1.0. It was found that the failure of actual slope can be simulated by ANN-based estimation.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.157-157
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• 2022

Recent decades have seen all over the world increasing drought in some regions and increasing flood in others. Climate change has been alarming in many regions resulting in degradation and diminution of available freshwater. The effect of global warming and overpopulation associated with increasing irrigated farming and valuable agricultural lands could be particularly disastrous for coastal areas like the one of Benin. The coastal region of Benin is under a heavy demographic pressure and was in the last decades the object of important urban developments. The present study aims to roughly study the general effect of climate change (Sea Level Rise: SLR) and groundwater pumping on Seawater intrusion (SWI) in Benin's coastal region. To reach the main goal of our study, the region aquifer system was built in numerical model using SEAWAT engine from Visual MODFLOW. The model is built and calibrated from 2016 to 2020 in SEAWAT, and using WinPEST the model parameters were optimized for a better performance. The optimized parameters are used for seawater intrusion intensity evaluation in the coastal region of Benin The simulation of the hydraulic head in the calibration period, showed groundwater head drawdown across the area with an average of 1.92m which is observed on the field by groundwater level depletion in hand dug wells mainly in the south of the study area. SWI area increased with a difference of 2.59km² between the start and end time of the modeling period. By considering SLR due to global warming, the model was stimulated to predict SWI area in 2050. IPCC scenario IS92a simulated SLR in the coastal region of Benin and the average rise is estimated at 20cm by 2050. Using the average rise, the model is run for SWI area estimation in 2050. SWI area in 2050 increased by an average of 10.34% (21.04 km²); this is expected to keep increasing as population grows and SLR.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.129-129
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• 2022

국내에서 발생하는 토양침식(soil erosion)은 주로 강우에 의해 발생하며, 이로 인해 농경지 유실, 탁수 발생, 하천 통수능 저하 등 여러 수문학적·환경적 문제가 발생한다. 따라서 유역 내 토양침식 위험지역을 선별하고, 해당 지역의 토양유실 및 유사의 발생량을 산정하는 것은 토양보전 대책 수립 시에 중요한 지표로 활용된다. 침식-유사유출의 물리적 과정은 크게 '강우에 의한 토양 분리(detachment by raindrop)'와 '지표류에 의한 토양 분리(detachment by overlandflow)'로 나눌 수 있으며, 그중 강우에 의한 토양 분리는 수침식(water erosion)의 첫 번째 과정 중 하나로 강우 시 낙하하는 강우 입자들이 갖는 운동에너지가 지표면을 타격할 때 토양체로부터 토양입자가 분리되는 과정이다. 따라서 강우에 의한 토양분리량 산정을 위해서는 강우 운동에너지(rainfall kinetic energy, KE)의 정확한 계산이 요구된다. 그러나 기후 및 지리적 특성 등 여러 조건에 따라 강우 운동에너지는 지역마다 다르게 나타나며, 이로 인해 강우 운동에너지 추정이 매우 어려운 실정이다. 따라서 강우 운동에너지 추정은 주로 강우강도(rainfall intensity, I)와의 관계를 이용한 함수식을 활용한다. 본 연구에서는 대상 지역인 상주지역에 광학우적계(disdrometer)를 설치하여 2020년 6월부터 2021년 12월까지 관측된 37개의 강우 사상에 대하여 KE-I의 관계를 분석하고, 이를 통해 강우 운동에너지식을 도출하였다. 또한, 기존에 국외 및 국내에서 제시된 선형(linear), 멱함수(power-law function), 지수함수(exponential function) 형태의 강우 운동에너지 공식과 본 연구에서 산정된 KE를 비교하였다. 그 결과 비체적 강우 운동에너지에서 Sanchez-Moreno et al. (2012)가 제안한 멱함수 형태의 공식이, 비시간 강우 운동에너지에서 Kinnel (1981)이 제안한 지수함수 형태의 공식이 각각 강우 운동에너지 추정에 통계적으로 유의한 것으로 나타났다.

• Korean Journal of Agricultural Science
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• v.51 no.1
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• pp.79-86
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• 2024

As the frequency and intensity of heavy rains increase, the vulnerability of agriculture to disasters also increases. Consequently, there is a need to improve flood and inundation predictions. To enhance the accuracy of inundation predictions, it is essential to monitor water level and discharge data within agricultural areas. This study was conducted to monitor water levels and rainfall in the Cheongju Sindae area from 2022 to 2023, and the data was utilized as input and validation data for agricultural inundation modeling. Four irrigation drainage canals were installed to a square-shaped concrete structure where the water level gauge is. It was then confirmed that the water level rises with rainfall. The flow velocities were monitored during periods of heavy rainfall. The rating curve, which estimates water level and flow velocity based on observations, was estimated using the software K-HQ. The resulting curve was presented with the Coefficient of Determination (R²). K-HQ was also used to calculate the equation for the rating curve, taking outliers into account at each data point. Outliers were extracted and the rating curve was recalculated. As the coefficient of determination of three out of four stations exceeded 0.95, the estimated rating curve may be considered reliable for discharge estimation. This study provides critical data for enhancing agricultural inundation modeling accuracy and drainage improvement projects.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1153-1164
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• 2024

In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.

• Geomechanics and Engineering
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• v.38 no.3
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• pp.215-229
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• 2024

Because of the various patterns of deep-water inrush and complicated mechanisms, accurately predicting mine water inflows is always a difficult problem for coal mine geologists. In study presented in this paper, the water inrush channels were divided into four basic water diversion structures: aquifer, rock fracture zone, fracture zone and goaf. The fluid flow characteristics in each water-conducting structure were investigated by laboratory tests, and multistructure and multisystem coupling flow analysis models of different water-conducting structures were established to describe the entire water inrush process. Based on the research of the water inrush flow paths, the analysis model of different water inrush space structures was established and applied to the prediction of mine water inrush inflow. The results prove that the conduction sequence of different water-conducting structures and the changing rule of permeability caused by stress changes before and after the peak have important influences on the characteristics of mine water-gushing. Influenced by the differences in geological structure and combined with rock mass RQD and fault conductivity characteristics and other mine exploration data, the prediction of mine water inflow can be realized accurately. Taking the water transmitting path in the multistructure as the research object of water inrush, breaking through the limitation of traditional stratigraphic structure division, the prediction of water inflow and the estimation of potentially flooded area was realized, and water bursting intensity was predicted. It is of great significance in making reasonable emergency plans.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.403-417
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• 2023

In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.