• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2034-2042
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• 2006

In this study, a complete 3D surface reconstruction method is proposed based on the concept that the vertices, of surface model can be completely matched to the unstructured point cloud. In order to generate the initial mesh model from the point cloud, the mesh subdivision of bounding box and shrink-wrapping algorithm are introduced. The control mesh model for well representing the topology of point cloud is derived from the initial mesh model by using the mesh simplification technique based on the original QEM algorithm, and the parametric surface model for approximately representing the geometry of point cloud is derived by applying the local subdivision surface fitting scheme on the control mesh model. And, to reconstruct the complete matching surface model, the insertion of isolated points on the parametric surface model and the mesh optimization are carried out. Especially, the fast 3D surface reconstruction is realized by introducing the voxel-based nearest-point search algorithm, and the simulation results reveal the availability of the proposed surface reconstruction method.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.11a
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• pp.69-70
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• 2018

With increased interest in the Fourth Industrial Revolution, there is also a growing interest in the innovation of college education. In this regard, this study aims to develop a learning model for building construction to nurture architectural engineers needed in the era of the Fourth Industrial Revolution. To this end, it analyzed the previous studies on the recent innovations in engineering education. Among the educational innovation methods presented in the previous research, a new learning model was derived by using the most suitable method for the building construction eduction. The derived learning model is a building construction learning model applying team-based learning and flipped learning. The learning model proposed in this study was developed as a learning method to nurture engineers needed in the future. Therefore, it is expected that this model can be utilized in the eduction of architectural engineering at universities in Korea.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.383-388
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• 2011

In this paper, we consider Hammerstein-Wiener nonlinear model for solid oxide fuel cell (SOFC). A nonlinear model predictive control (MPC) is proposed to trace the constant stack terminal power by Hydrogen flow as control input. After the stability of the closed-loop system with static output feedback controller is analysed by Lyapunov method, a nonlinear model predictive control based on the Hammerstein-Wiener model is developed to control the stack terminal power of the SOFC system. Simulation results verify the effectiveness of the proposed control method based on the Hammerstein-Wiener model for SOFC system.

• Proceedings of the KIEE Conference
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• summer
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• pp.430-432
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• 2004

The standard power system model is needed to test a transmission line protective relay There are two methods to develop a power system model for transmission line protection. First method is based on characteristic power system model, and second method is based on functional power system model. This paper presents a standard power system model for performance test of transmission line protective relay, where the power system model is based on the two methods. And this model is simulated by using RTDS to test a protective relay.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.149-161
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• 2019

In this article, we suggest the following approaches to simultaneous variable selection and outlier detection. First, we determine possible candidates for outliers using properties of an intercept estimator in a difference-based regression model, and the information of outliers is reflected in the multiple regression model adding mean shift parameters. Second, we select the best model from the model including the outlier candidates as predictors using stochastic search variable selection. Finally, we evaluate our method using simulations and real data analysis to yield promising results. In addition, we need to develop our method to make robust estimates. We will also to the nonparametric regression model for simultaneous outlier detection and variable selection.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.99-102
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• 2003

A new interacting multiple model (IMM) method using genetic algorithm (GA)-based intelligent input estimation(IIE) is proposed to track a maneuvering target. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown acceleration input by a fuzzy system using the relation between maneuvering filter residual and non-maneuvering one. The GA is utilized to optimize a fuzzy system fur a sub-model within a fixed range of acceleration input. Then, multiple models are composed of these fuzzy systems, which are optimized for different ranges of acceleration input. In computer simulation for an incoming ballistic missile, the tracking performance of the proposed method is compared with those of the input estimation(IE) technique and the adaptive interacting multiple model (AIMM) method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.825-829
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• 2009

In this paper, we examine the fundamental performance of image coding schemes based on multipulse model. First, we introduce several kinds of pulse search methods (i.e., correlation method, pulse overlap search method and pulse amplitude optimization method) for the model. These pulse search methods are derived from auto-correlation function of impulse responses and cross-correlation function between host signals and impulse responses. Next, we explain the basic procedure of multipulse image coding scheme, which uses the above pulse search methods in order to encode the high frequency component of an original image. Finally, by means of computer simulation for some test images, we examine the PSNR(Peak Signal-to-Noise Ratio) and computational complexity of these methods.

• Smart Structures and Systems
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• v.25 no.5
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• pp.569-580
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• 2020

The identification of delays and delay compensation are critical problems in real-time hybrid simulations (RTHS). Conventional delay compensation methods are mostly based on the assumption of a constant delay. However, the system delay may vary during tests owing to the nonlinearity of the loading system and/or the behavioral variations of the specimen. To address this issue, this study presents an adaptive delay compensation method based on a discrete model of the loading system. In particular, the parameters of this discrete model are identified and updated online with the least-squares method to represent a servo hydraulic loading system. Furthermore, based on this model, the system delays are compensated for by generating system commands using the desired displacements, achieved displacements, and previous displacement commands. This method is more general than the existing compensation methods because it can predict commands based on multiple displacement categories. Moreover, this method is straightforward and suitable for implementation on digital signal processing boards because it relies solely on the displacements rather than on velocity and/or acceleration data. The virtual and real RTHS results show that the studied method exhibits satisfactory estimation smoothness and compensation accuracy. Furthermore, considering the measurement noise, the low-order parameter models of this method are more favorable than that the high-order parameter models.

• Smart Structures and Systems
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• v.29 no.1
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• pp.29-39
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• 2022

Crack detection plays an important role in the maintenance and protection of steel box girder of bridges. However, since the cracks only occupy an extremely small region of the high-resolution images captured from actual conditions, the existing methods cannot deal with this kind of image effectively. To solve this problem, this paper proposed a novel three-stage method based on deep learning technology and morphology operations. The training set and test set used in this paper are composed of 360 images (4928 × 3264 pixels) in steel girder box. The first stage of the proposed model converted high-resolution images into sub-images by using patch-based method and located the region of cracks by CBAM ResNet-50 model. The Recall reaches 0.95 on the test set. The second stage of our method uses the Attention U-Net model to get the accurate geometric edges of cracks based on results in the first stage. The IoU of the segmentation model implemented in this stage attains 0.48. In the third stage of the model, we remove the wrong-predicted isolated points in the predicted results through dilate operation and outlier elimination algorithm. The IoU of test set ascends to 0.70 after this stage. Ablation experiments are conducted to optimize the parameters and further promote the accuracy of the proposed method. The result shows that: (1) the best patch size of sub-images is 1024 × 1024. (2) the CBAM ResNet-50 and the Attention U-Net achieved the best results in the first and the second stage, respectively. (3) Pre-training the model of the first two stages can improve the IoU by 2.9%. In general, our method is of great significance for crack detection.

• Smart Structures and Systems
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• v.26 no.3
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• pp.291-309
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• 2020

To study oceanic and meteorological problems related to climate change, Korea has been operating several ocean research stations (ORSs). In 2011, the Gageocho ORS was attacked by Typhoon Muifa, and its structural members and several observation devices were severely damaged. After this event, the Gageocho ORS was rehabilitated with 5 m height to account for 100-yr extreme wave height, and the vibration measurement system was equipped to monitor the structural vibrational characteristics including natural frequencies and modal damping ratios. In this study, a mass reallocation method is presented for structural model updating of the Gageocho ORS based on the experimentally identified natural frequencies. A preliminary finite element (FE) model was constructed based on design drawings, and several of the candidate baseline FE models were manually built, taking into account the different structural conditions such as corroded thickness. Among these candidate baseline FE models, the most reasonable baseline FE model was selected by comparing the differences between the identified and calculated natural frequencies; the most suitable baseline FE model was updated based on the identified modal properties, and by using the pattern search method, which is one of direct search optimization methods. The mass reallocation method is newly proposed as a means to determine the equivalent mass quantities along the height and in a floor. It was found that the natural frequencies calculated based on the updated FE model was very close to the identified natural frequencies. In conclusion, it is expected that these results, which were obtained by updating a baseline FE model, can be useful for establishing the reference database for jacket-type offshore structures, and assessing the structural integrity of the Gageocho ORS.