• Nuclear Engineering and Technology
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• 제54권1호
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• pp.220-225
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• 2022

Perlite is one of the major constituents of the radioactive thermal insulation waste (RTIW) originating from nuclear power plants and, for proper waste management, a significant reduction in its volume is required prior to disposal. In this work, the volume reduction of perlite is studied by high-temperature treatment method with using K₂CO₃ as a flux. The perlite is ground with 0-30 wt% K₂CO₃, and differential thermal analysis/thermogravimetric analysis is used to monitor the glass transition temperature (T_g) and weight loss. The Tg varied between ~772.2 and 837.1 ℃ with the minima at ~643.5 ℃ with the addition of ~10 wt% K₂CO₃. It is observed that compared to the pure perlite the volume reduction ratio (VRR) increases with the addition of K₂CO₃. The VRR of 11.20 is observed with 5 wt% K₂CO₃ at 700 ℃, as compared to VRR of 5.56 without K₂CO₃ at 700 ℃. The X-ray photoelectron spectroscopy and scanning electron microscopy are used to characterize perlite samples heat-treated without/with 5 wt% K₂CO₃ at 700 ℃. Moreover, the atomic absorption spectroscopy indicates that the proposed heat-treatment procedure is able to completely retain the radionuclides present in the perlite RTIW.

• 지질공학
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• 제31권4호
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• pp.541-547
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• 2021

비탈면의 유실, 붕락사고 등 비탈면에 대한 안정성을 파악하기 위해서 지층의 구성상태, 역학적 특성 등의 지반정보 파악이 필요하다. 이러한 지반정보를 파악하기 위해서 일반적으로 표준관입시험(SPT) 및 콘 관입시험 등이 널리 이용되고 있다. 대부분이 급경사로 이루어지고 진입로가 없는 비탈면에 대한 접근성 문제로 표준관입시험이 널리 활용되지 못하고 있다. 이러한 단점을 보완할 수 있는 있는 휴대용 장비인 Drop Cone Penetrometer(DCP)를 이용한 조사도 여러 가지 문제로 제한적으로 사용되고 있다. 따라서 본 연구에서는 비탈면 현장접근이 용이한 휴대용 시추기와 동적콘관입시험 모듈을 개발하고, 개발된 동적콘관입시험기를 이용한 결과와 동일 현장에서 수행한 표준관입시험값과 상관성을 분석하였다. 에너지전단율로 보정된 동적콘관입시험과 표준관입시험간의 상관식은 N_d' = 3.13 N'으로 나타났다.

• 한국인터넷방송통신학회논문지
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• 제22권4호
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• pp.67-72
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• 2022

본 논문에서는 60 nm GaN/Si HEMT 공정을 사용하여 전력증폭기(Power Amplifier)의 설계를 제시하였다. 고주파 설계를 위하여 맞춤형 트랜지스터 모델을 구성하였다. Output stage는 저손실 설계를 위해 마이크로스트립 라인을 사용하여 회로를 구성하였다. 또한 RC 네트워크로 구성된 Bias Feeding Line과 Input bypass 회로의 AC Ground(ACGND) 회로를 각각 적용하여 DC 소스에 연결된 노드의 최소임피던스가 RF회로에 영향을 미치지 않도록 하였다. 이득과 출력을 고려하여 3단의 구조로 설계되었다. 설계된 전력증폭기의 최종 사이즈는 3900 ㎛ × 2300 ㎛ 이다. 중심 주파수에서 설계된 결과는 12 V의 공급 전압에서 15.9 dB의 소 신호 이득, 29.9 dBm의 포화 출력(Psat), 24.2 %의 PAE를 달성하였다.

• Geomechanics and Engineering
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• 제31권6호
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제12권8호
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• pp.243-252
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• 2023

위성 이미지는 토지 표면 조사에서 매우 중요하다. 따라서 위성에서 지상국으로 이미지를 전송하기 위해 다양한 방법을 사용하고 있다. 그러나 전송 시스템의 품질 저하로 인해 이미지는 왜곡에 취약하고 올바른 데이터를 제공하지 못하고 있다. 그러한 이미지의 세그먼트 결과는 토지 표면 데이터를 올바르게 분류할 수 없다. 본 논문에서는 위성영상에 대한 자동인코더 기반의 영상 전처리 방법을 제안한다. 실험결과 사전 향상 기술을 사용하여 세그멘테이션 결과도 크게 향상될 수 있음을 보여주었다. 또한 본 논문에서 적용한 항공 이미지 향상기법은 토지 자원의 정확한 평가에 이바지할 수 있음을 확인하였다.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.696-706
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• 2023

Consequences of an anticipated Beyond Design Basis Accident (BDBA) Long-Term Station Blackout (LTSBO) event with complete loss of grid power in the VVER-1200 reactor of Rooppur Nuclear Power Plant (NPP) of Unit-1 are assessed using the RASCAL 4.3 code. This study estimated the released radionuclides, received public radiological dose, and ground surface concentration considering 3 accident scenarios of International Nuclear and Radiological Event Scale (INES) level 7 and two meteorological conditions. Atmospheric transport, dispersion, and deposition processes of released radionuclides are simulated using a straight-line trajectory Gaussian plume model for short distances and a Gaussian puff model for long distances. Total Effective Dose Equivalent (TEDE) to the public within 40 km and radionuclides contribution for three-dose pathways of inhalation, cloudshine, and groundshine owing to airborne releases are evaluated considering with and without passive safety Emergency Core Cooling System (ECCS) in dry (winter) and wet (monsoon) seasons. Source term and their release rates are varied with the functional duration of passive safety ECCS. In three accident scenarios, the TEDE of 10 mSv and above are confined to 8 km and 2 km for the wet and dry seasons, respectively in the downwind direction. The groundshine dose is the most dominating in the wet season while the inhalation dose is in the dry season. Total received doses and surface concentration in the wet season near the plant are higher than those in the dry season due to the deposition effect of rain on the radioactive substances.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.172-172
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• 2020

In recent years, soil erosion has come to be regarded as an essential environmental problem in human life. Soil erosion causes various on- and off-site problems such as ecosystem destruction, decreased agricultural productivity, increased riverbed deposition, and deterioration of water quality in streams. To solve these problems caused by soil erosion, it is necessary to quantify where, when, how much soil erosion occurs. Empirical erosion models such as the Universal Soil Loss Equation (USLE) family models have been widely used to make spatially distributed soil erosion vulnerability maps. Even if the models detect vulnerable sites relatively well by utilizing big data related to climate, geography, geology, land use, etc. within study domains, they do not adequately describe the physical process of soil erosion on the ground surface caused by rainfall or overland flow. In other words, such models remain powerful tools to distinguish erosion-prone areas at the macro scale but physics-based models are necessary to better analyze soil erosion and deposition and eroded particle transport. In this study, the physics-based Surface Soil Erosion Model (SSEM) was upgraded based on field survey information to produce sediment yield at the watershed scale. The modified model (hereafter MoSE) adopted new algorithms on rainfall kinematic energy and surface flow transport capacity to simulate soil erosion more reliably. For model validation, we applied the model to the Doam dam watershed in Gangwon-do and compared the simulation results with the USLE outputs. The results showed that the revised physics-based soil erosion model provided more improved and reliable simulation results than the USLE in terms of the spatial distribution of soil erosion and deposition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.2924-2944
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• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• 한국터널지하공간학회 논문집
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• 제25권6호
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• pp.459-468
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• 2023

본연구에서는 국내에서 많이 활용되고 있는 시멘트 모르타르 및 수지와 같은 그라우팅재를 이용한 정착형 록볼트와 새롭게 제시되는 탄성복원력을 이용한 강관 마찰형 록볼트에 대한 인발력실험을 수행하고 그 결과를 비교분석 하였다. 지하수가 없는 건조한 조건에서 실험한 결과, 기존 시멘트 모르타르를 이용한 록볼트에 대한 인발력이 레진을 이용한 록볼트와 마찰형 강관 록볼트와 비교하여 측정한 인발력이 상대적으로 크게 나타났다. 그럼에도 불구하고 탄성복원력을 이용한 마찰형 강관 록볼트는 특히 지하수가 존재하여 그라우팅재료의 손실과 양생에 영향을 미치는 현장조건에서 유용하게 사용할 수 있을 것으로 판단된다. 이와 더불어 마찰형 강관 록볼트는 설치가 간편하고 빠른 장점을 가질 수 있는 것으로 나타났다.

• 드라이브 ㆍ 컨트롤
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• 제21권2호
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• pp.23-29
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• 2024

Potatoes are one of the world's four major crops, and domestic consumption is currently increasing in Korea. However, the mechanization rate of potatoes is very low, and especially, harvesting is the most labor-intensive task in potato production. In Korea, potato-collecting work depends on manpower, so it is necessary to develop a gathering-type harvester that can be used for processes from digging to harvesting. Therefore, in this study, a self-propelled-type potato harvester was developed, and its performance was analyzed to mechanize harvesting. The potato harvester was developed to have a crawler-type driving part with a 60 hp diesel engine and consisted of a digging part that digs potatoes from the ground, a vertical transporting part that transfers the dug potatoes to the height of the collection bag, a separating part that separates debris, such as stones and soil, and a collecting part that loads the collection box. A field test of the potato harvester was conducted, and performance was evaluated by the damage, loss, and debris mixing proportions, which were 2.5%, 2.8%, and 2.6%, respectively. The working capacity was 1.2 h/10 a. The economic analysis results showed that the cost of harvesting work could be reduced by 12.7% compared to manual harvesting.