• Geomechanics and Engineering
• /
• v.32 no.4
• /
• pp.411-426
• /
• 2023

Soft clay is widely distributed in the southeast coastal areas of China. Many large underground structures, such as subway stations and underground pipe corridors, are shallow buried in the soft clay foundation, so the dynamic characteristics of the soft clay must be considered to the seismic design of underground structures. In this paper, the dynamic characteristics of saturated soft clay in Shanghai under the bidirectional excitation for earthquake loading are studied by dynamic triaxial tests, comparing the backbone curve and hysteretic curve of the saturated soft clay under different confining pressures with those under different vibration frequencies. Considering the coupling effects of the confining pressure and the vibration frequency, a fitting model of the maximum dynamic shear modulus was proposed by the multiple linear regression method. The M-D model was used to fit the variations of the dynamic shear modulus ratio with the shear strain. Based on the Chen model and the Park model, the effects of the consolidation confining pressure and the vibration frequency on the damping ratio were studied. The results can provide a reference to the earthquake prevention and disaster reduction in soft clay area.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2010.03a
• /
• pp.188-197
• /
• 2010

This paper presents experimental results of a series of 1-g shaking table model tests performed on end-bearing single piles and pile groups to investigate the effect of particle size on the dynamic behavior of soil-pile systems. Two soil-pile models consisting of a single-pile and a $4{\times}2$-pile group were tested twice; first using Jumoonjin sand, and second using Australian Fine sand, which has a smaller particle size. In the case of single-pile models, the lateral displacement was almost within 1% of pile diameter which corresponds to the elastic range of the pile. The back-calculated p-y curves show that the subgrade reaction of the Jumoonjin-sand-model ground was larger than that of the Australian Fine-sand-model ground at the same displacement. This phenomenon means that the stress-strain behavior of Jumoonjin sand was initially stiffer than that of Australian Fine sand. This difference was also confirmed by resonant column tests and compression triaxial tests. And the single pile p-y backbone curves of the Australian fine sand were constructed and compared with those of the Jumoonjin sand. As a result, the stiffness of the p-y backbone curves of Jumunjin sand was larger than those of Australian fine sand. Therefore, using the same p-y curves regardless of particle size can lead to inaccurate results when evaluating dynamic behavior of soil-pile system. In the case of the group-pile models, the lateral displacement was much larger than the elastic range of pile movement at the same test conditions in the single-pile models. The back-calculated p-y curves in the case of group pile models were very similar in both sands because the stiffness difference between the Jumoonjin-sand-model ground and the Australian Fine-sand-model ground was not significantly large at a large strain level, where both sands showed non-linear behavior. According to a series of single pile and group pile test results, the evaluation group pile effect using the p-multiplier can lead to inaccurate results on dynamic behavior of soil-pile system.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.11a
• /
• pp.625-627
• /
• 2022

In the construction and development of modern industrial production technology, the traditional technology management mode is faced with many problems such as low qualification rates and high application costs. In the research, an improved workpiece defect detection method based on deep learning is proposed, which can control the application cost and improve the detection efficiency of irregular defects. Based on the research of the current situation of deep learning applications, this paper uses the improved Faster R-CNN network structure model as the core detection algorithm to automatically locate and classify the defect areas of the workpiece. Firstly, the robustness of the model was improved by appropriately changing the depth and the number of channels of the backbone network, and the hyperparameters of the improved model were adjusted. Then the deformable convolution is added to improve the detection ability of irregular defects. The final experimental results show that this method's average detection accuracy (mAP) is 4.5% higher than that of other methods. The model with anchor size and aspect ratio (65,129,257,519) and (0.2,0.5,1,1) has the highest defect recognition rate, and the detection accuracy reaches 93.88%.

• The Korean Journal of Pesticide Science
• /
• v.2 no.3
• /
• pp.45-51
• /
• 1998

A series of bis-aromatic ${\alpha},{\beta}$-unsaturated ketone derivatives with mesaured fungicidal activities in vivo against rice blast(Pyricularia oryzae), tomato leaf blight (Phytophtora infestans) and barley powdery mildew(Erysiphe graminis) were studied by using quantitative structure activity relationship equations. The QSAR model for the activity of phenyl substituents, $1{\sim}11$, clearly reveals three important factors, namely, resonance(R<0), optimal molecular hydrophobicity(${\pi})_{opt.}=0.38$) and optimal distance($((L_{1})_{opt.}=5.69({\AA}))$ of substituent, respectively. But in case of 2-pyridyl substituents, $12{\sim}28$, the activity were governed by optimal molecular refractivity $((M_{R})_{opt.}=8.04{\sim}39cm^{3}/mol)$, steric effect(Es<0) and LUMO energy(e.v). The fungicidal activity relationship of phenyl and 2-pyridyl substituents against Erysiphe graminis have been proportioned.

• Proceedings of the Korean Biophysical Society Conference
• /
• 2003.06a
• /
• pp.75-75
• /
• 2003

We report here the results on N-acetyl-N'-dimethylamide of proline (Ac-Pro-NM $e_2$) calculated using the ab initio molecular orbital method with the self-consistent reaction field (SCRF) theory at the HF level with the 6-31+G(d) basis set to investigate the conformational preference of polyproline depending on the cis/trans peptide bonds and down/up puckerings along the backbone torsion angle $\square$ in the gas phase, chloroform, and water. In the gas phase, Ac-Pro-NM $e_2$ has seven local minima of tFd, tFu, cFd, cFu, cAu, tAu, and cAd conformations. In particular, polyproline conformations tFd, tFu, cFd, and cFu are found to be more stable than $\square$-helical conformations cAu, tAu, and cAd. In contrast, Ac-Pro-NHMe has seven local minima of tCd, tCu, cBd, cAu, tAu, cFd, and cFu conformations. Conformations tCd and tCu are found to be most stable, which is ascribed to the intramolecular hydrogen bond between C=O of acetyl group and $N^{~}$ H of N'-methyl amide group. The stability of the cFd conformation (i.e., the polyproline I structure) in chloroform is somewhat increased, relative to that in water, although tFd and tFu conformations (i.e., the polyproline II structure) are dominate both in chloroform and water. The population of backbone conformations feasible in chloroform and water is consistent with the experiments. This work is supported by a Korea Research Foundation Grant (KRF-2002-041-C00129).

• Journal of Communications and Networks
• /
• v.18 no.4
• /
• pp.559-566
• /
• 2016

Software defined network (SDN) can effectively improve the performance of traffic engineering and will be widely used in backbone networks. Therefore, new energy-saving schemes must take SDN into consideration; this action is extremely important owing to the rapidly increasing energy consumption in telecom and Internet service provider (ISP) networks. Meanwhile, the introduction of SDN in current networks must be incremental in most cases, for technical and economic reasons. During this period, operators must manage hybrid networks in which SDN and traditional protocols coexist. In this study, we investigate the energy-efficient traffic engineering problem in hybrid SDN/Internet protocol (IP) networks. First, we formulate the mathematical optimization model considering the SDN/IP hybrid routing mode. The problem is NP-hard; therefore, we propose a fast heuristic algorithm named hybrid energy-aware traffic engineering (HEATE) as a solution. In our proposed HEATE algorithm, the IP routers perform shortest-path routing by using distributed open shortest path first (OSPF) link weight optimization. The SDNs perform multipath routing with traffic-flow splitting managed by the global SDN controller. The HEATE algorithm determines the optimal setting for the OSPF link weight and the splitting ratio of SDNs. Thus, the traffic flow is aggregated onto partial links, and the underutilized links can be turned off to save energy. Based on computer simulation results, we demonstrate that our algorithm achieves a significant improvement in energy efficiency in hybrid SDN/IP networks.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.43 no.4
• /
• pp.511-523
• /
• 2023

Research on the integration of unmanned aerial vehicles and deep learning for reinforced concrete damage detection is actively underway. Convolutional neural networks have a high impact on the performance of image classification, detection, and segmentation as backbones. The MobileNet, a pre-trained convolutional neural network, is efficient as a backbone for an unmanned aerial vehicle-based damage detection model because it can achieve sufficient accuracy with low computational complexity. Analyzing vanilla convolutional neural networks and MobileNet under various conditions, MobileNet was evaluated to have a verification accuracy 6.0~9.0% higher than vanilla convolutional neural networks with 15.9~22.9% lower computational complexity. MobileNetV2, MobileNetV3Large and MobileNetV3Small showed almost identical maximum verification accuracy, and the optimal conditions for MobileNet's reinforced concrete damage image feature extraction were analyzed to be the optimizer RMSprop, no dropout, and average pooling. The maximum validation accuracy of 75.49% for 7 types of damage detection based on MobilenetV2 derived in this study can be improved by image accumulation and continuous learning.

• Structural Engineering and Mechanics
• /
• v.85 no.4
• /
• pp.469-484
• /
• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.

• Journal of the Korean Magnetic Resonance Society
• /
• v.18 no.1
• /
• pp.24-29
• /
• 2014

Atomistic molecular dynamics (MD) simulation has become mature enabling close approximation of the real behaviors of biomolecules. In biomolecular NMR field, atomistic MD simulation coupled with generalized implicit solvent model (GBIS) has contributed to improving the qualities of NMR structures in the refinement stage with experimental restraints. Here all-atom force fields play important roles in defining the optimal positions between atoms and angles, resulting in more precise and accurate structures. Despite successful applications in refining NMR structure, however, the research that has studied the influence of force fields in GBIS is limited. In this study, we compared the qualities of NMR structures of two model proteins, ubiquitin and GB1, under a series of AMBER force fields-ff99SB, ff99SB-ILDN, ff99SB-NMR, ff12SB, and ff13-with experimental restraints. The root mean square deviations of backbone atoms and packing scores that reflect the apparent structural qualities were almost indistinguishable except ff13. Qualitative comparison of parameters, however, indicates that ff99SB-ILDN is more recommendable, at least in the cases of ubiquitin and GB1.

• Journal of the Korean Magnetic Resonance Society
• /
• v.17 no.1
• /
• pp.11-18
• /
• 2013

Rapid advances of computational power and method have made it practical to apply the time-consuming calculations with all-atom force fields and sophisticated potential energies into refining NMR structure. Added to the all-atom force field, generalized-Born implicit solvent model (GBIS) contributes substantially to improving the qualities of the resulting NMR structures. GBIS approximates the effects that explicit solvents bring about even with fairly reduced computational times. Although GBIS is employed in the final stage of NMR structure calculation with experimental restraints, the effects by GBIS on structures have been reported notable. However, the detailed effect is little studied in a quantitative way. In this study, we report GBIS refinements of ubiquitin and GB1 structures by six GBIS models of AMBER package with experimental distance and backbone torsion angle restraints. Of GBIS models tested, the calculations with igb=7 option generated the closest structures to those determined by X-ray both in ubiquitin and GB1 from the viewpoints of root-mean-square deviations. Those with igb=5 yielded the second best results. Our data suggest that the degrees of improvements vary under different GBIS models and the proper selection of GBIS model can lead to better results.