• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2629-2639
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• 2021

Within the frame of the EMPIrE test, four monolithic mini-plates were irradiated in the ATR reactor. In two of them, the monolithic U(Mo) foil had been PVD-coated with Zr before the plate manufacturing. Extensive microstructural characterizations were performed on a fresh archive mini-plate, using Optical Microscopy (OM), Scanning Electron Microscopy (SEM) combined with Energy Dispersive Spectroscopy (EDS), Electron Backscattered Diffraction (EBSD) and Focused Ion Beam (FIB)/Transmission Electron Microscopy (TEM) with nano EDS. A particular attention was paid to the examination of the U(Mo) foil, the PVD coating, the cladding/Zr and Zr/U(Mo) interfaces. The Zr coating has a thickness around 15 ㎛. It has a columnar microstructure and appears dense. The cohesion of the cladding/Zr and Zr/U(Mo) interfaces seems to be satisfactory. An almost continuous layer with a thickness of the order of 100-300 nm is present at the cladding/Zr interface and corresponds to an oxidized part of the Zr coating. At the Zr/U(Mo) interface, a thin discontinuous layer is observed. It could correspond to locally oxidized U(Mo). This work provides a basis for interpreting the results of characterizations on EMPIrE irradiated plates.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 추계학술대회
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• pp.628-631
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• 2014

최근 다양한 형태의 스마트 기기 출현과 대중적 보급으로 인해 고속 데이터 전송에 대한 수요가 나날이 증가하고 있다. 이런 요구사항을 수용하기 위해 셀룰러 사업자 및 이동통신 장비 제조업체는 많은 새로운 기술에 대한 연구를 진행하였으며 이에 대한 결과로 향후 셀룰러 네트워크에서 성능 및 커버리지 향상을 위해 소형셀 기술을 하나의 중요한 요소 기술로 고려하고 있다. 셀룰러 네트워크에서 소형셀 기술은 데이터 요구량이 많은 위치에 소형셀을 밀집 배치하고 매크로 기지국 및 소형셀 기지국의 밀접한 협력을 통해 무선 네트워크의 용량을 증가시키는 것을 의미한다. 따라서 본 논문에서는 매크로 셀과 소형 셀이 다층으로 배치된 셀룰러 이동통신 구조를 제시하고 다층셀간의 협력을 통해 성능을 향상시킬 수 있는 다양한 요소기술들에 대해서 기술한다. 또, 이들 요소기술들을 바탕으로 최근 3GPP에서 활발히 논의되고 있는 LTE 소형셀 향상 표준화 동향에 대해 기술한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2131-2153
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• 2022

To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest X-ray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.

• Nuclear Engineering and Technology
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• 제54권6호
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• pp.1972-1981
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• 2022

A typical ferritic/martensitic (F/M) steel sheet was subjected to pulsed laser surface remelting (LSR) and corrosion test in lead-bismuth eutectic (LBE) at 550 ℃. There present two modification zones with distinct microstructures in the LSRed specimen: (1) remelted zone (RZ) consisting of both bulk δ-ferrite grains and martensitic plates and (2) heat-affected zone (HAZ) below the RZ, mainly composed of martensitic plates and high-density precipitates. Martensitic transformation occurs in both the RZ and the HAZ with the Kurdjumov-Sachs and Nishiyama-Wassermann orientation relationships followed concurrently, resulting in scattered orientations and specific misorientation characteristics. Hardnesses of the RZ and the HAZ are 364 ± 7 HV and 451 ± 15 HV, respectively, considerably higher than that of the matrix (267 ± 3 HV). In oxygen-saturated and oxygen-depleted LBE, thicknesses of oxide layers developed on both the as-received and the LSRed specimens increase with prolonging corrosion time (oxide layers always thinner under the oxygen-depleted condition). The corrosion resistance of the LSRed F/M steel in oxygen-saturated LBE is improved, which can be attributed to the grain-refinement accelerated formation of dense Fe-Cr spinel. In oxygen-depleted LBE, the growth of oxide layers is very low with both types of specimens showing similar corrosion resistance.

• Smart Structures and Systems
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• 제30권6호
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• 대한의용생체공학회:의공학회지
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• 제43권6호
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• pp.390-398
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• 2022

Ultrasound examination using ultrasound equipment is an ultrasound device that images human organs using sound waves and is used in various areas such as diagnosis, follow-up, and treatment of diseases. However, if the quality of ultrasound equipment is not guaranteed, the possibility of misdiagnosis increases, and the diagnosis rate decreases. Accordingly, The Korean Society of Radiology and Korea society of Ultrasound in Medicine presented guidelines for quality management of ultrasound equipment using ATS-539 phantom. The DenseNet201 classification algorithm shows 99.25% accuracy and 5.17% loss in the Dead Zone, 97.52% loss in Axial/Lateral Resolution, 96.98% accuracy and 20.64% loss in Sensitivity, 93.44% accuracy and 22.07% loss in the Gray scale and Dynamic Range. As a result, it is the best and is judged to be an algorithm that can be used for quantitative evaluation. Through this study, it can be seen that if quantitative evaluation using artificial intelligence is conducted in the qualitative evaluation item of ultrasonic equipment, the reliability of ultrasonic equipment can be increased with high accuracy.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

• Smart Structures and Systems
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• 제31권4호
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• pp.335-349
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• 2023

Bridges constantly undergo deterioration and damage, the most common ones being concrete damage and exposed rebar. Periodic inspection of bridges to identify damages can aid in their quick remediation. Likewise, identifying components can provide context for damage assessment and help gauge a bridge's state of interaction with its surroundings. Current inspection techniques rely on manual site visits, which can be time-consuming and costly. More recently, robotic inspection assisted by autonomous data analytics based on Computer Vision (CV) and Artificial Intelligence (AI) has been viewed as a suitable alternative to manual inspection because of its efficiency and accuracy. To aid research in this avenue, this study performs a comparative assessment of different architectures, loss functions, and ensembling strategies for the autonomous segmentation of bridge components and damages. The experiments lead to several interesting discoveries. Nested Reg-UNet architecture is found to outperform five other state-of-the-art architectures in both damage and component segmentation tasks. The architecture is built by combining a Nested UNet style dense configuration with a pretrained RegNet encoder. In terms of the mean Intersection over Union (mIoU) metric, the Nested Reg-UNet architecture provides an improvement of 2.86% on the damage segmentation task and 1.66% on the component segmentation task compared to the state-of-the-art UNet architecture. Furthermore, it is demonstrated that incorporating the Lovasz-Softmax loss function to counter class imbalance can boost performance by 3.44% in the component segmentation task over the most employed alternative, weighted Cross Entropy (wCE). Finally, weighted softmax ensembling is found to be quite effective when used synchronously with the Nested Reg-UNet architecture by providing mIoU improvement of 0.74% in the component segmentation task and 1.14% in the damage segmentation task over a single-architecture baseline. Overall, the best mIoU of 92.50% for the component segmentation task and 84.19% for the damage segmentation task validate the feasibility of these techniques for autonomous bridge component and damage segmentation using RGB images.

• 멤브레인
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• 제33권4호
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• pp.168-180
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• 2023

본 총설은 탄소중립 및 에너지순환을 실현하기 위한 재생에너지로부터 그린수소 생산 전략 중 하나인 바이오수소 생산 및 정제법에 관해 소개하고자 한다. 바이오수소는 생물질과 미생물과 같은 재생에너지원을 이용하며, 상온 및 상압 등의 마일드한 실험조건에서 작동하여 에너지소비 및 공정비용이 적게 드는 친환경 공정으로 알려져 있다. 하지만, 이러한 바이오수소를 상업적으로 이용하기 위해서는 해결해야 할 중요한 도전적인 과제가 존재한다. 특히, 바이오수소는 생물반응기내의 복합한 화학반응으로 합성되어, 낮은 수소생산 속도 및 반응기내 다양한 혼합물이 존재하여, 바이오수소 고순도화를 위해서 연속공정 형태의 분리 및 정제 기술이 반드시 필요하다. 이를 위해, 저온 증류법, 압력 흡착법, 분리막법 등을 비롯한 다양한 분리 및 정제 기술이 고순도 바이오수소를 얻기 위해 제안되었다. 본 총설에서는 바이오수소 생산 및 정제 연계화를 위한 비다공성 고분자 분리막의 가능성에 대해 소개하고자 한다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.279-282
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• 2022

급성 림프모구성 백혈병은 골수 내 미성숙 림프구 과다증식으로 인해 골수 기능이 억제되어 발생하는 급성 백혈병이다. 성인 급성 백혈병의 30% 비율을 차지하고 있으며, 소아는 항암화학요법으로 80% 이상의 완치율을 보이는 반면, 성인은 20%~50%로 저조한 생존율을 보이고 있다. 그러나 급성 림프모구성 백혈병 진단을 위한 의료영상 데이터 기반 머신러닝 알고리즘에 관한 연구가 초동 단계이다. 본 논문에서는 신속하고 정확한 진단을 위해 CNN 알고리즘모델들을 비교분석한다. 네 가지 모델을 사용하여 급성 림프모구성 백혈병 진단 모델들을 비교분석하기 위한 실험 환경을 구축하고 주어진 의료영상 데이터에 대해 정확도가 가장 우수한 알고리즘을 선택하였다. 실험 결과에 따르면 네 가지의 CNN 모델들 중에서 InceptionV3모델이 98.9%의 정확도로 가장 우수한 성능을 보였다.