• Pediatric Gastroenterology, Hepatology & Nutrition
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• 제24권5호
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• pp.483-491
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• 2021

Purpose: Obesity has become a very significant health problem in childhood. Fructose taken in an uncontrolled manner and consumed in excessive amounts is rapidly metabolized in the body and gets converted into fatty acids. This single center prospective case-control study aims to investigate the relationship between fructose consumption and obesity and the role of fructose consumption in development of atherosclerotic diseases. Methods: A total of 40 obese and 40 healthy children who were of similar ages (between 8 and 18 years) and sexes were included in the study. In the patient and control groups, the urine fructose levels, as well as the levels of oxidized low-density lipoprotein (LDL), small dense LDL, Apolipoprotein A and Apolipoprotein B values, which have been shown to play a role in development of atherosclerotic diseases, were measured. Results: The levels of oxidized LDL and small dense LDL and the ratio of Apolipoprotein A/Apolipoprotein B were found to be significantly higher in the patient group. Conclusion: We found that urinary fructose levels were higher in the obese children than the healthy children. Our results suggest that overconsumption of fructose in children triggers atherogenic diseases by increasing the levels of small dense LDL and oxidized LDL and the ratio of Apolipoprotein B/Apolipoprotein A.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.110-132
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• 2019

To ensure reliable and secure communication in heterogeneous cellular network (HCN) with imperfect channel state information (CSI), we proposed a coordinated multipoint (CoMP) transmission scheme based on dual-threshold optimization, in which only base stations (BSs) with good channel conditions are selected for transmission. First, we present a candidate BSs formation policy to increase access efficiency, which provides a candidate region of serving BSs. Then, we design a CoMP networking strategy to select serving BSs from the set of candidate BSs, which degrades the influence of channel estimation errors and guarantees qualities of communication links. Finally, we analyze the performance of the proposed scheme, and present a dual-threshold optimization model to further support the performance. Numerical results are presented to verify our theoretical analysis, which draw a conclusion that the CoMP transmission scheme can ensure reliable and secure communication in dense HCNs with imperfect CSI.

• 자연, 터널 그리고 지하공간
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• 제21권1호
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• pp.101-112
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• 2019

This case study presented a simplified failure mechanism approach used as a preliminary deformation prediction for the Mexico City's metro system expansion. Because of the Mexico City's difficult subsoils, Line 12 project was considered one of the most challenging projects in Mexico. Mexico City's subsurface conditions can be described as a multilayered stratigraphy changing from soft high plastic clays to dense to very dense cemented sands. The Line 12 trajectory crossed all three main geotechnical Zones in Mexico City. Starting from to west of the City, Line 12 was projected to pass through very dense cemented sands corresponding to the Foothills zone changing to the Transition zone and finalizing in the Lake zone. Due to the change in the subsurface conditions, different constructions methods were implemented including the use of TBM (Tunnel Boring Machine), the NATM (New Austrian Tunneling Method), and cut-and-cover using braced Diaphragm walls for the underground section of the project. Preliminary crown and excavation front deformations were determined using a simplified failure mechanism prior to performing finite element modeling and analysis. Results showed corresponding deformations for the crown and the excavation front to be 3.5cm (1.4in) and 6cm (2.4in), respectively. Considering the complexity of Mexico City's difficult subsoil formation, construction method selection becomes a challenge to overcome. The use of a preliminary results in order to have a notion of possible deformations prior to advanced modeling and analysis could be beneficial and helpful to select possible construction procedures.

• 한국측량학회지
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• 제39권2호
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• pp.73-79
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• 2021

Recently there are growing interests on the energy conservation and emission reduction. In the fields of architecture and civil engineering, the energy monitoring of structures is required to response the energy issues. In perspective of thermal monitoring, thermal images gains popularity for their rich visual information. With the rapid development of the drone platform, aerial thermal images acquired using drone can be used to monitor not only a part of structure, but wider coverage. In addition, the stereo photogrammetric process is expected to generate 3D point cloud with thermal information. However thermal images show very poor in resolution with narrow field of view that limit the use of drone-based thermal photogrammety. In the study, we aimed to generate 3D thermal point cloud using visible and thermal images. The visible images show high spatial resolution being able to generate precise and dense point clouds. Then we extract thermal information from thermal images to assign them onto the point clouds by precisely establishing photogrammetric collinearity between the point clouds and thermal images. From the experiment, we successfully generate dense 3D thermal point cloud showing 3D thermal distribution over the building structure.

• 소성∙가공
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• 제31권4호
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• pp.229-239
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• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.656-658
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• 2022

The purpose of infrared and visible image fusion is to integrate images of different modes with different details into a result image with rich information, which is convenient for high-level computer vision task. Considering many deep networks only work in a single scale, this paper proposes a novel image fusion based on three-scale dense network to preserve the content and key target features from the input images in the fused image. It comprises an encoder, a three-scale block, a fused strategy and a decoder, which can capture incredibly rich background details and prominent target details. The encoder is used to extract three-scale dense features from the source images for the initial image fusion. Then, a fusion strategy called l₁-norm to fuse features of different scales. Finally, the fused image is reconstructed by decoding network. Compared with the existing methods, the proposed method can achieve state-of-the-art fusion performance in subjective observation.

• Asian Pacific Journal of Cancer Prevention
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• 제16권14호
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• pp.6011-6017
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• 2015

Purpose: To investigate the efficacy and safety of selective radiotherapy after distant metastasis of nasopharyngeal carcinoma (NPC) treated with dose-dense cisplatin plus fluorouracil. Materials and Methods: Eligible patients were randomly assigned to a study group treated with dose-dense cisplatin plus fluorouracil following selective radiotherapy and a control group receiving traditional cisplatin plus fluorouracil following selective radiotherapy according to a 1:1 distribution using a digital random table method. The primary endpoint was overall survival (OS). Secondary endpoints were progression-free survival (PFS), objective response rate, relapse or progression rate in the radiation field and treatment toxicity. Results: Of 52 patients in the study group, 20 cases underwent radiotherapy., while in the control group of 51 patients, 16 underwent radiotherapy. The median PFS, median OS, survival rates in 1, 2 and 3 years in study and control group were 20.9 vs 12.7months, 28.3 vs 18.8months, 85.2%vs 65.9%, 62.2% vs 18.3%, and 36.6%vs 5.2% (p values of 0.00, 0.00, 0.04, 0.00 and 0.00, respectively). Subgroup analysis showed that the median OS and survival rates of 1, 2, 3 years for patients undergoing radiotherapy in the study group better than that in control group( 43.2vs24.1 months, 94.1% vs 86.7%, 82.4% vs 43.3%, 64.7% vs 17.3%, (p=0.00, 0.57, 0.04 and 0.01, respectively). The complete response rate, objective response rate after chemotherapy and three months after radiotherapy, relapse or progression rate in radiation field in study group and in control group were 19.2% vs 3.9%, 86.5% vs 56.9%, 85% vs 50%, 95% vs 81.3% and 41.3% vs 66.7% (p =0.03, 0.00, 0.03,0.30, 0.01 respectively). The grade 3-4 acute adverse reactions in the study group were significantly higher than in the control group (53.8% vs 9.8%, p=0.00). Conclusions: The survival of patients benefits from selective radiotherapy after distant metastasis of NPC treated with dose-dense cisplatin plus fluorouracil.

• Weed & Turfgrass Science
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• 제5권1호
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• pp.17-22
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• 2016

장성지역 잔디 재배지에서 총생 증상의 들잔디가 발생하였다. 총생 증상의 들잔디는 작고 다발형태로 유의한 형태적인 변화를 보였고, 병징은 여러 개의 잎이 짧은 포복경 마디에 무더기로 발생되어 빗자루처럼 총생 형태를 보였으며, 총생 형태의 잔디는 잎이 황화되면서 연녹색이나 노란색을 띄었다. 총생 증상을 보이는 들잔디는 건전한 들잔디에 비해 포복경 1개 마디당 약 5.8배 이상의 잎이 발생하였다. 또한 과도한 지상부 생육으로 인해 포복경과 지하부 생장이 저조하였다. 총생 병징의 들잔디를 채집하여 병원균을 배양한 후 관찰한 결과 포자낭의 모양은 레몬모양이었으며, 포자낭 축의 끝이 뾰족하게 튀어나와 있었고, 크기는 $60{\sim}96{\times}42{\sim}51{\mu}m$로 관찰되었다. 이는 Sclerophthora macrospora 병원균이 형성하는 포자낭의 균학적 특징과 유사하였고, 봄과 가을에 병징을 확인할 수 있었다. 따라서 본 연구를 통하여 국내 잔디 재배지 총생 증상에 대한 기초적인 자료를 제시하고자 한다.

• Journal of Animal Science and Technology
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• 제63권2호
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• pp.367-379
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• 2021

The objectives of this study were to evaluate convolutional neural network models and computer vision techniques for the classification of swine posture with high accuracy and to use the derived result in the investigation of the effect of dietary fiber level on the behavioral characteristics of the pregnant sow under low and high ambient temperatures during the last stage of gestation. A total of 27 crossbred sows (Yorkshire × Landrace; average body weight, 192.2 ± 4.8 kg) were assigned to three treatments in a randomized complete block design during the last stage of gestation (days 90 to 114). The sows in group 1 were fed a 3% fiber diet under neutral ambient temperature; the sows in group 2 were fed a diet with 3% fiber under high ambient temperature (HT); the sows in group 3 were fed a 6% fiber diet under HT. Eight popular deep learning-based feature extraction frameworks (DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, VGG16, VGG19, and Xception) used for automatic swine posture classification were selected and compared using the swine posture image dataset that was constructed under real swine farm conditions. The neural network models showed excellent performance on previously unseen data (ability to generalize). The DenseNet121 feature extractor achieved the best performance with 99.83% accuracy, and both DenseNet201 and MobileNet showed an accuracy of 99.77% for the classification of the image dataset. The behavior of sows classified by the DenseNet121 feature extractor showed that the HT in our study reduced (p < 0.05) the standing behavior of sows and also has a tendency to increase (p = 0.082) lying behavior. High dietary fiber treatment tended to increase (p = 0.064) lying and decrease (p < 0.05) the standing behavior of sows, but there was no change in sitting under HT conditions.

• 대한원격탐사학회지
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• 제39권2호
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• pp.183-192
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• 2023

위성영상 및 항공사진과 같은 원격탐사 자료들은 영상판독과 영상처리 기법을 통하여 영상 내의 객체를 탐지하고 추출하는 데에 사용될 수 있다. 특히, 원격탐사 자료의 해상도가 향상되고, 딥러닝(deep learning) 모델 등과 같은 기술의 발전으로 인하여 관심객체를 자동으로 추출하여 지도갱신 및 지형 모니터링 등에 활용될 수 있는 가능성이 증대되고 있다. 이를 위해, 본 연구에서는 의미론적 분할에 사용되는 대표적인 딥러닝 모델인 fully convolutional densely connected convolutional network (FC-DenseNet)을 기반으로 하여 항공정사영상 내 존재하는 비닐하우스를 추출하고, 이에 대한 결과를 정량적으로 평가하였다. 농림축산식품부의 팜맵(farm map)을 이용하여 담양, 밀양지역의 비닐하우스에 대한 레이블링을 수행하여 훈련자료를 생성하고, 훈련자료를 이용하여 FC-DenseNet의 훈련을 수행하였다. 원격탐사자료에 딥러닝 모델을 효과적으로 이용하기 위하여, 각 밴드별 특성이 유지되도록 instance norm을 이용하여 정규화과정을 수행하였으며, attention module을 추가하여 각 밴드별 가중치를 효과적으로 산정하였다. 실험결과, 딥러닝 모델을 이용하여 영상 내 존재하는 비닐하우스 지역을 효과적으로 추출할 수 있음을 확인하였으며 팜맵, 토지피복지도 등의 갱신에 활용될 수 있을 것으로 판단하였다.