• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.323-330
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• 2021

Visual object tracking is a challenging area of study in the field of computer vision due to many difficult problems, including a fast variation of target shape, occlusion, and arbitrary ground truth object designation. In this paper, we focus on the reinforced feature of the dynamic search area to get better performance than conventional discriminative model prediction trackers on the condition when the accuracy deteriorates since low feature discrimination. We propose a reinforced input feature method shown like the spotlight effect on the dynamic search area of the target tracking. This method can be used to improve performances for deep learning based discriminative model prediction tracker, also various types of trackers which are used to infer the center of the target based on the visual object tracking. The proposed method shows the improved tracking performance than the baseline trackers, achieving a relative gain of 38% quantitative improvement from 0.433 to 0.601 F-score at the visual object tracking evaluation.

• Structural Engineering and Mechanics
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• v.63 no.2
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• pp.237-249
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• 2017

The Self-Organization Feature Map as an unsupervised network is very widely used these days in engineering science. The applied network in this paper is the Self Organization Feature Map with constant weights which includes Kohonen Network. In this research, Reinforced Concrete Shear Wall buildings with different stories and heights are analyzed and a database consisting of measured fundamental periods and characteristics of 78 RC SW buildings is created. The input parameters of these buildings include number of stories, height, length, width, whereas the output parameter is the fundamental period. In addition, using Genetic Algorithm, the structure of the Self-Organization Feature Map algorithm is optimized with respect to the numbers of layers, numbers of nodes in hidden layers, type of transfer function and learning. Evaluation of the SOFM model was performed by comparing the obtained values to the measured values and values calculated by expressions given in building codes. Results show that the Self-Organization Feature Map, which is optimized by using Genetic Algorithm, has a higher capacity, flexibility and accuracy in predicting the fundamental period.

• Earthquakes and Structures
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• v.27 no.3
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• pp.177-189
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• 2024

Accurately predicting the failure modes of reinforced concrete (RC) columns is essential for structural design and assessment. In this study, the challenges of imbalanced datasets and complex feature selection in machine learning (ML) methods were addressed through an optimized ML approach. By combining feature selection and oversampling techniques, the prediction of seismic failure modes in rectangular RC columns was improved. Two feature selection methods were used to identify six input parameters. To tackle class imbalance, the Borderline-SMOTE1 algorithm was employed, enhancing the learning capabilities of the models for minority classes. Eight ML algorithms were trained and fine-tuned using k-fold shuffle split cross-validation and grid search. The results showed that the artificial neural network model achieved 96.77% accuracy, while k-nearest neighbor, support vector machine, and random forest models each achieved 95.16% accuracy. The balanced dataset led to significant improvements, particularly in predicting the flexure-shear failure mode, with accuracy increasing by 6%, recall by 8%, and F1 scores by 7%. The use of the Borderline-SMOTE1 algorithm significantly improved the recognition of samples at failure mode boundaries, enhancing the classification performance of models like k-nearest neighbor and decision tree, which are highly sensitive to data distribution and decision boundaries. This method effectively addressed class imbalance and selected relevant features without requiring complex simulations like traditional methods, proving applicable for discerning failure modes in various concrete members under seismic action.

• Smart Structures and Systems
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• v.32 no.4
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• pp.207-218
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• 2023

Real-time monitoring of the behavior of reinforced soil retaining wall (RSW) is required for safety checks. In this study, a targetless displacement measurement technology (TDMT) consisting of an image registration module and a displacement calculation module was proposed to monitor the behavior of RSW, in which facing displacement and settlement typically occur. Laboratory and field experiments were conducted to compare the measuring performance of natural target (NT) with the performance of artificial target (AT). Feature count- and location-based performance metrics and displacement calculation performance were analyzed to determine their correlations. The results of laboratory and field experiments showed that the feature location-based performance metric was more relevant to the displacement calculation performance than the feature count-based performance metric. The mean relative errors of the TDMT were less than 1.69 % and 5.50 % for the laboratory and field experiments, respectively. The proposed TDMT can accurately monitor the behavior of RSW for real-time safety checks.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.04a
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• pp.321-326
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• 1998

Shear failure of reinforced concrete beams is serious problem due to sudden brittle failure and many experimental results proved that size effect in shear behavior is an important feature of reinforced concrete members. For this reason, the structural safety of the reinforced concrete beams for shear has been checked by applying empirical design formula, which includes the size-effect, derived from experimental data. However, as the sizes of reinforced concrete members become extremely large, experiments sometimes become very difficult so that the formula or the experimental data could not be obtained and size-effect analyses of shear behavior become significant. In this study, size-effect analysis of shear behavior in reinforced concrete beams is performed by modeling tension stiffening/shear stiffening on reinforced concrete and the tension softening/shear softening on plain concrete. Then, the influences of models in the size-effect analyses of shear behavior in reinforced concrete beams are analyzed.

• Journal of the Korean Society of Safety
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• v.16 no.1
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• pp.88-93
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• 2001

This study discusses frequency analysis based on autoregressive (AR) time series model, and process characterization in orthogonal cutting of a fiber-matrix composite materials. A sparsely distributed idealized composite material, namely a glass reinforced polyester (GFRP) was used as workpiece. Analysis method employs a force sensor and the signals from the sensor are processed using AR time series model. The resulting pattern vectors of AR coefficients are then passed to the feature extraction block. Inside the feature extraction block, only those features that are most sensitive to different types of cutting mechanisms are selected. The experimental correlations between the different chip formation mechanisms and AR model coefficients are established.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.10a
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• pp.323-328
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• 2001

In the ordinary reinforced earth wall, which was constructed by incremental construction method, the horizontal deformation of the facing due to the compaction induced horizontal earth pressure was unavoidable. Thus the KOESWall system which are adopted the isolated construction method was developed by I&S Eng. Co., Ltd. in 1999. Due to its systematical feature, KOESWall system is able to minimizes the horizontal deformation of reinforced wall effectively and it can be used as temporary structures more economically without the lacing block. In this report, it is shown that the concept and case histories of KOESWall system as a retaining structures.

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

• Steel and Composite Structures
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• v.17 no.6
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• pp.777-790
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• 2014

The purpose of this study is to investigate the seismic performance of reinforced concrete (RC) frames strengthened by profiled steel sheet bracing which takes the influence of infill walls into consideration. One-bay, two-story, 1/3 scale two specimens shared same feature of dimensions, one specimen consists only beams and columns; the other one is reinforced by profiled steel sheet bracing with infill walls. Hysteretic curves, envelope curves, stiffness degradation curves and energy dissipation capacities are presented based on test data. Test results indicate that the ultimate load of strengthened specimen has been improved by 225%. The stiffness of reinforced by profiled steel sheet bracing has been increased by 108%. This demonstrates that infill walls and profiled steel sheet bracing enhanced the strength and stiffness distinctly. Energy dissipation has an obvious increase after 12 cycles. This shows that the reinforced specimen is able to bear the lateral load effectively and absorb lots of seismic energy.

• Coupled systems mechanics
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• v.3 no.1
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• pp.111-145
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• 2014

In this paper we present a new model for computing the nonlinear response of reinforced concrete frame systems subjected to extreme thermomechanical loads. The first main feature of the model is its ability to account for both bending and shear failure of the reinforced concrete composites within frame-like model. The second prominent feature concerns the model capability to represent the total degradation of the material properties due to high temperature and the thermal deformations. Several numerical simulations are given to confirm these capabilities and illustrate a very satisfying model performance.