• Journal of the Korea Concrete Institute
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• v.21 no.5
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• pp.599-609
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• 2009

This paper discusses generalized modeling schemes for both reinforced concrete (RC) and post-tensioned (PT) flat plate buildings. In this modeling approach, nonlinear behavior due to slab flexure, moment and shear transfer at slab-column connections, and punching shear was included along with linear secant stiffness of each member or connection that accounts for concrete cracking. This generalized model was capable of simulating all different scenarios of slab-column connection failures such as brittle punching, flexure-shear interactive failure, and flexural failure followed by drift-induced punching. Furthermore, automatic detection of drift-induced punching shear and subsequent backbone curve modifications were realistically modelled by incorporating the limit state model, in which gravity shear versus drift capacity relations were adopted. The validation of the model was conducted using one-third scale two-story by two-bay RC and PT flat plate frames. The comparisons revealed that the model was robust and effective.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10B
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• pp.947-956
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• 2003

The current IP Routing Protocolspacket networks also need to provide the network QoS based of DiffServ, RSVP, MPLStraffic model which is standardized as IETF reference model for NGN. The first topic of this paper is to propose Traffic-Balanced Routing Protocol(TBRP) to process existing best effort traffic. TBRP will process low priority interactive data and background data which is not sensitive to dealy. Secondly Hierarchical Traffic-Traffic-Scheduling Routing Protocol(HTSRP) is also proposed. HTSRP is the hierarchical routing algorithm for backbone and access networkin case of fixed-wireless convergence network. Finally, HTSRP_Q is proposed to meet the QoS requirement when user want interactive or streaming packet service. This protocol will maximize the usage of resources of access layer based on the QoS parameters and process delay-sensitive traffic. Service classes are categorized into 5 types by the user request, such as conversational, streaming, high priority interactive, low priority interactive, and background class. It could be processed efficiently by the routing protocolstraffic model proposed in this paper. The proposed routing protocolstraffic model provides the increase of efficiency and stability of the next generation network thanks to the routing according to the characteristic of the specialized service categories.

• Steel and Composite Structures
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• v.30 no.3
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• pp.201-215
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• 2019

In this research, the behavior of tube-in-tube BRBs (TiTBRBs) has been investigated. In a typical TiTBRB, the yielding core tube is located inside the outer restraining one to dissipate energy through extensive plastic deformation, while the outer restraining tube remains essentially elastic. With the aid of FE analyses, the monotonic and cyclic behavior of the proposed TiTBRBs have been studied as individual brace elements. Subsequently, a detailed finite element model of a representative single span-single story frame equipped with such a TiTBRB has been constructed and both monotonic and cyclic behavior of the proposed TiTBRBs have been explored under the application of the AISC loading protocol at the braced frame level. With the aid of backbone curves derived from the FE analyses, a simplified frame model has been developed and verified through comparison with the results of the detailed FE model. It has been shown that, the simplified model is capable of predicting closely the cyclic behavior of the TiTBRB frame and hence can be used for design purposes. Considering type of connection detail used in a frame, the TiTBRB member which behave satisfactorily at the brace element level under cyclic loading conditions, may suffer global buckling due to the flexural demand exerted from the frame to the brace member at its ends. The proposed TiTBRB suit tubular members of offshore structures and the application of such TiTBRB in a typical offshore platform has been introduced and studied in a single frame level using detailed FE model.

• Journal of Communications and Networks
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• v.17 no.1
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• pp.58-66
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• 2015

Aeronautical communication networks (ACN) is an emerging concept in which aeronautical stations (AS) are considered as a part of multi-tier network for the future wireless communication system. An AS could be a commercial plane, helicopter, or any other low orbit station, i.e., Unmanned air vehicle, high altitude platform. The goal of ACN is to provide high throughput and cost effective communication network for aeronautical applications (i.e., Air traffic control (ATC), air traffic management (ATM) communications, and commercial in-flight Internet activities), and terrestrial networks by using aeronautical platforms as a backbone. In this paper, we investigate the issues about connectivity, throughput, and delay in ACN. First, topology of ACN is presented as a simple mobile ad hoc network and connectivity analysis is provided. Then, by using information obtained from connectivity analysis, we investigate two communication models, i.e., single-hop and two-hop, in which each source AS is communicating with its destination AS with or without the help of intermediate relay AS, respectively. In our throughput analysis, we use the method of finding the maximum number of concurrent successful transmissions to derive ACN throughput upper bounds for the two communication models. We conclude that the two-hop model achieves greater throughput scaling than the single-hop model for ACN and multi-hop models cannot achieve better throughput scaling than two-hop model. Furthermore, since delay issue is more salient in two-hop communication, we characterize the delay performance and derive the closed-form average end-to-end delay for the two-hop model. Finally, computer simulations are performed and it is shown that ACN is robust in terms of throughput and delay performances.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.976-984
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• 2009

This study proposes a simple method that uses a simple mass-spring model to predict the natural frequency of a soil-pile-structure system in sandy soil. This model includes a pair of matrixes, i.e., a mass matrix and a stiffness matrix. The mass matrix is comprised of the masses of the pile and superstructure, and the stiffness matrix is comprised of the stiffness of the pile and the spring coefficients between the pile and soil. The key issue in the evaluation of the natural frequency of a soil-pile system is the determination of the spring coefficient between the pile and soil. To determine the reasonable spring coefficient, subgrade reaction modulus, nonlinear p-y curves and elastic modulus of the soil were utilized. The location of the spring was also varied with consideration of the infinite depth of the pile. The natural frequencies calculated by using the mass-spring model were compared with those obtained from 1-g shaking table model pile tests. The comparison showed that the calculated natural frequencies match well with the results of the 1-g shaking table tests within the range of computational error when the three springs, whose coefficients were calculated using Reese's(1974) subgrade reaction modulus and Yang's (2009) dynamic p-y backbone curves, were located above the infinite depth of the pile.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.81-89
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• 2021

Under natural conditions, Apple detection has the problems of occlusion and small object detection difficulties. This paper proposes an improved model based on SSD. The SSD backbone network VGG16 is replaced with the ResNet50 network model, and the receptive field structure RFB structure is introduced. The RFB model amplifies the feature information of small objects and improves the detection accuracy of small objects. Combined with the attention mechanism (SE) to filter out the information that needs to be retained, the semantic information of the detection objectis enhanced. An improved SSD algorithm is trained on the VOC2007 data set. Compared with SSD, the improved algorithm has increased the accuracy of occlusion and small object detection by 3.4% and 3.9%. The algorithm has improved the false detection rate and missed detection rate. The improved algorithm proposed in this paper has higher efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.1976-1995
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• 2023

The anchor-free object detector CenterNet regards the object as a center point and predicts it based on the Gaussian circle region. For each object's center point, CenterNet directly regresses the width and height of the objects and finally gets the boundary range of the objects. However, the critical range of the object's center point can not be accurately limited by using the Gaussian circle region to constrain the prediction region, resulting in many low-quality centers' predicted values. In addition, because of the large difference between the width and height of different objects, directly regressing the width and height will make the model difficult to converge and lose the intrinsic relationship between them, thereby reducing the stability and consistency of accuracy. For these problems, we proposed a center point prediction method based on the Gaussian elliptic region and a size component regression method based on the small solution space. First, we constructed a Gaussian ellipse region that can accurately predict the object's center point. Second, we recode the width and height of the objects, which significantly reduces the regression solution space and improves the convergence speed of the model. Finally, we jointly decode the predicted components, enhancing the internal relationship between the size components and improving the accuracy consistency. Experiments show that when using CenterNet as the improved baseline and Hourglass-104 as the backbone, on the MS COCO dataset, our improved model achieved 44.7%, which is 2.6% higher than the baseline.

• Structural Engineering and Mechanics
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• v.91 no.5
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• pp.443-457
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• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• Bulletin of the Korean Chemical Society
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• v.35 no.9
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• pp.2787-2790
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• 2014

Quercetin is a major dietary flavonoid found in onions, apples, tea, and red wine, and potentially has beneficial effects on disease prevention. We carried out this study to investigate the effect of quercetin on UVB-induced matrix metalloproteinase-1 (MMP-1) expression in human keratinocyte HaCaT cells and to further understand the mechanisms of its action. The anti-inflammatory activity of quercetin was investigated and quercetin significantly suppressed the NO production in LPS-stimulated RAW264.7 mouse macrophages. Post treatment of quercetin decreased UV irradiation-induced phosphorylation of JNK, p38 MAPK, and ERK by 91%, 21%, and 17%, respectively. MMP-1 is mainly responsible for the degradation of dermal collagen during the aging process of human skin and quercetin suppressed the UVB-induced MMP-1 by 94%. Binding studies revealed that quercetin binds to p38 with high binding affinity ($1.85{\times}10^6M^{-1}$). The binding model showed that the 4'-hydroxy groups of the B-ring of quercetin participated in hydrogen bonding interactions with the side chains of Lys53, Glu71, and Asp168 and the 5-hydroxy group of the A-ring formed a hydrogen bond with the backbone amide of Met109. The major finding of this study shows that quercetin inhibits phosphorylation of JNK, p38 MAPK, and ERK pathway leading to the prevention of MMP-1 expression in human keratinocyte HaCaT cells. Therefore, our findings suggested the potentials of quercetin as a skin anti-photoaging agent.

• Bulletin of the Korean Chemical Society
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• v.35 no.7
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• pp.1944-1950
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• 2014

Employment of a time consuming, sophisticated calculation using the all-atom force field and generalized-Born implicit solvent model (GBIS) for refinement of NMR structures has become practical through advances in computational methods and capacities. GBIS refinement improves the qualities of the resulting NMR structures with reduced computational times. However, the contribution of GBIS to NMR structures has not been sufficiently studied in a quantitative way. In this paper, we report the effects of GBIS on the refined NMR structures of ubiquitin (UBQ) and GB1 with subsets of distance restraints derived from experimental data. Random omission prepared a series of distance restraints 0.05, 0.1, 0.3, 0.5, and 0.7 times smaller. For each number, we produced five different restraints for statistical analysis. We then recalculated the NMR structures using CYANA software, followed by GBIS refinements using the AMBER package. GBIS improved both the precision and accuracy of all the structures, but to varied levels. The degrees of improvement were significant when the input restraints were insufficient. In particular, GBIS enabled GB1 to form an accurate structure even with distance restraints of 5%, revealing that the root-mean-square deviation was less than 1 ${\AA}$ from the X-ray backbone structure. We also showed that the efficiency of searching the conformational space was more important for finding accurate structures with the calculation of UBQ with 5% distance restraints than the number of conformations generated. Our data will provide a meaningful guideline to judge and compare the structural improvements by GBIS.