• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.24 no.5
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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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• v.13 no.1
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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.

• Magazine of korean Tunnelling and Underground Space Association
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• v.21 no.1
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.2
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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.

• Transactions of Materials Processing
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• v.31 no.4
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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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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• v.16 no.14
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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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• v.5 no.1
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• pp.17-22
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• 2016

Yellow tuft symptoms of a dense cluster on zoysiagrass (Zoysia japonica Steud.) occurred extensively at cultivated fields of zoysiagrass sods in Jangsung. The dense cluster of zoysiagrass showed significant morphological changes such as the tufts of shortening of internodes. The disease symptom was spread on a large scale throughout stolon nodes with multiple short leaves and it thrives in broom-like shaped clusters, exhibiting light green or yellow color on their leaves. The dense cluster of zoysiagrass had approximately 5.8 times more leaves on each node of its stolon then healthy zoysiagrass. Also, these zoysiagrass had poorly developed root and stolon caused by the tufts of a dense cluster of shoots. The dense cluster of zoysiagrass were collected for the putative causal agent incubation and upon close observation, it was found that the sporangia took the shape of a lemon, each sporangium was pointed at the end of its axis and was measured to be $60{\sim}96{\times}42{\sim}51{\mu}m$. These findings were analogous to the mycological characteristics of sporangia formed by the pathogen Sclerophthora macrospora. The symptoms of yellow tuft were prevalent in spring and autumn. Therefore, this study aims to present fundamental data in relation to yellow tuft on zoysiagrass in Korea.

• Journal of Animal Science and Technology
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• v.63 no.2
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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.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.