• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.6
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• pp.601-608
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• 2011

This paper presents the damage assessment of a building structure by using a novel optical fiber accelerometer system. Especially, a sub-scaled building model is designed and manufactured to check up the feasibility of the optical fiber accelerometer for structural health monitoring. The novel accelerometer exploits the moir$\acute{e}$ fringe optical phenomenon and two pairs of optical fibers to measure the displacement with a high accuracy, and furthermore a pendulum to convert the displacement into acceleration. A prototype of optical fiber accelerometer system has been successfully developed that consists of a sensor head, a control unit and a signal processing unit. The building model is also designed as a 4-story building with a rectangular shape of $200{\times}300$ mm of edges. Each floor is connected to the next ones by 6 steel columns which are threaded rods. Basically, a random vibration test of the building model is done with a shaker and all of acceleration data is successfully measured at the assigned points by the optical fiber accelerometer. The experiments are repeated in the undamaged state and the damaged state. The comparison of dynamic parameters including the natural frequencies and the eigenvectors is successfully carried out. Finally, the optical fiber accelerometer is proven to be prospective to evaluate dynamic characteristics of a building structure for the damage assessment.

• 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.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.10 no.1
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• pp.75-81
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• 2014

A simulation of eddy current testing has been utilized for predicting the signal characteristics to the various defects and developing the probes. Especially, CIVA which is a simulation tool dedicated to nondestructive testing has a good accuracy and speed, and provides a three-dimensional graphical user interface for improved visualization and familiar data displays consistent with NDE technique. Although internal validations have been performed by the CIVA software development specialists, an independent validation study is necessary for the accuracy assessment of the software prior to practical use. For this purpose, in this study, eddy current testing signals of ASME FBH calibration standard tube for bobbin probe were simulated using CIVA and the results were compared to the experimental inspected signals based on the relationship between each flaw signal in terms of amplitude and phase, and the shape of the Lissajous curve. And then we verified the accuracy of the simulated signals and the possible range for simulation. Overall, there is a good qualitative agreement between the CIVA simulated and experimental results in the absolute and differential modes at the two inspection frequencies.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.393-402
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• 2003

In times of the civil uses of commercialized high-resolution satellite imagery, applications of remote sensing have been widely extended to the new fields or the problem solving beyond traditional application domains. Transportation application of this sensor data, related to the automatic or semiautomatic road extraction, is regarded as one of the important issues in uses of remote sensing imagery. Related to these trends, this study focuses on automatic road extraction using Gradient Direction Profile Algorithm (GDPA) scheme, with IKONOS panchromatic imagery having 1 meter resolution. For this, the GDPA scheme and its main modules were reviewed with processing steps and implemented as a prototype software. Using the extracted bi-level image and ground truth coming from actual GIS layer, overall accuracy evaluation and ranking error-assessment were performed. As the processed results, road information can be automatically extracted; by the way, it is pointed out that some user-defined variables should be carefully determined in using high-resolution satellite imagery in the dense or low contrast areas. While, the GDPA method needs additional processing, because direct results using this method do not produce high overall accuracy or ranking value. The main advantage of the GDPA scheme on road features extraction can be noted as its performance and further applicability. This experiment study can be extended into practical application fields related to remote sensing.

• International Journal of Concrete Structures and Materials
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• v.6 no.3
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• pp.165-176
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• 2012

The aim of this study is to assess the seismic performance of hollow reinforced concrete and prestressed concrete bridge columns, and to provide data for developing improved seismic design criteria. By using a sophisticated nonlinear finite element analysis program, the accuracy and objectivity of the assessment process can be enhanced. A computer program, RCAHEST (Reinforced Concrete Analysis in Higher Evaluation System Technology), is used to analyze reinforced concrete and prestressed concrete structures. Tensile, compressive and shear models of cracked concrete and models of reinforcing and prestressing steel were used to account for the material nonlinearity of reinforced concrete and prestressed concrete. The smeared crack approach was incorporated. The proposed numerical method for the seismic performance assessment of hollow reinforced concrete and prestressed concrete bridge columns is verified by comparing it with the reliable experimental results. Additionally, the studies and discussions presented in this investigation provide an insight into the key behavioral aspects of hollow reinforced concrete and prestressed concrete bridge columns.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.75-86
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• 2022

The existing image quality assessment (IQA) datasets have a small number of samples. Some methods based on transfer learning or data augmentation cannot make good use of image quality-related features. A No Reference (NR)-IQA method based on multi-task training and quality awareness is proposed. First, single or multiple distortion types and levels are imposed on the original image, and different strategies are used to augment different types of distortion datasets. With the idea of weak supervision, we use the Full Reference (FR)-IQA methods to obtain the pseudo-score label of the generated image. Then, we combine the classification information of the distortion type, level, and the information of the image quality score. The ResNet50 network is trained in the pre-train stage on the augmented dataset to obtain more quality-aware pre-training weights. Finally, the fine-tuning stage training is performed on the target IQA dataset using the quality-aware weights to predicate the final prediction score. Various experiments designed on the synthetic distortions and authentic distortions datasets (LIVE, CSIQ, TID2013, LIVEC, KonIQ-10K) prove that the proposed method can utilize the image quality-related features better than the method using only single-task training. The extracted quality-aware features improve the accuracy of the model.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.287-291
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• 2006

Currently application of high-resolution satellite imagery is expanding with development of high tech optical and space aviation technology. Although using 3 dimensional modeling technology in order to attain accurate terrain information using existing ground control points is the most dependable reference data, such means are unapplicable for certain area because of it's limited access. In this study, we have researched into ways to utilizing high resolution satellite images from IKONOS and Quickbird, and sub-meter class satellites images that will be utilized In the future such as Arirang images and PLEIADES images for unaccessible areas. For that purpose we have created accuracy verification and GCP files for existing ortho-imagery and digital elevation model. The results showed that accuracy of ortho-Imagery and digital elevation model was RMSE X:3.043m, Y:2.921m, Z:6.139m. Also, after ortho-rectifying IKONOS images using ground control points extracted from ortho imagery and digital elevation model the accuracy of the imagery was RMSE X:3.243m, Y:2.067m, Z:1.872m.

• Geomechanics and Engineering
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• v.13 no.2
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• pp.237-254
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• 2017

Estimation of slope stability is a very important task in geotechnical engineering. However, its estimation using conventional and soft computing methods has several drawbacks. Use of conventional limit equilibrium methods for the evaluation of slope stability is very tedious and time consuming, while the use of soft computing approaches like Artificial Neural Networks and Fuzzy Logic are black box approaches. Multiple Regression (MR) analysis provides an alternative to conventional and soft computing methods, for the evaluation of slope stability. MR models provide a simplified equation, which can be used to calculate critical factor of safety of slopes without adopting any iterative procedure, thereby reducing the time and complexity involved in the evaluation of slope stability. In the present study, a multiple regression model has been developed and tested its accuracy in the estimation of slope stability using real field data. Here, two separate multiple regression models have been developed for dry and wet slopes. Further, the accuracy of these developed models have been compared and validated with respect to conventional limit equilibrium methods in terms of Mean Square Error (MSE) & Coefficient of determination ($R^2$). As the developed MR models here are not based on any region specific data and covers wide range of parametric variations, they can be directly applied to any real slopes.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.11
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• pp.4913-4916
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• 2016

Objective: As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.

• Smart Structures and Systems
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• v.31 no.6
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• pp.601-619
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• 2023

In this study, the accuracy of a real-time hybrid test (RTHT) employed for a performance test of a tuned mass damper (TMD) on an offshore wind turbine (OWT) with a complicated jacket-type supporting structure is quantified and evaluated by comparing the RTHT results with the experimental data obtained from a shaking table test (STT), in which a 1/25-scale model for a typical 5-MW OWT controlled by a TMD was tested. In the RTHT, the jacket-type OWT structure was modelled using both multiple-DOF (MDOF) and single-DOF (SDOF) numerical models. When compared with the STT test data, the test results of the RTHT show that while the SDOF model, which requires less control computational time, is able to well predict the peak responses of the nacelle and TMD only, the MDOF model is able to effectively predict both the peak and over-all time-history responses at multiple critical locations of an OWT structure. This also indicates that, depending on the type of structural responses considered, an RTHT with either an SDOF or a MDOF model may be a promising alternative to the STT to assess the effectiveness of a TMD for seismic mitigation in an OWT context.