• Wind and Structures
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• v.39 no.2
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• pp.125-140
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• 2024

The simulation of non-stationary wind velocity is particularly crucial for the wind resistant design of slender structures. Recently, some data-driven simulation methods have received much attention due to their straightforwardness. However, as the number of simulation points increases, it will face efficiency issues. Under such a background, in this paper, a time-varying coherence matrix decomposition method based on Diagonal Proper Orthogonal Decomposition (DPOD) interpolation is proposed for the data-driven simulation of non-stationary wind velocity based on S-transform (ST). Its core idea is to use coherence matrix decomposition instead of the decomposition of the measured time-frequency power spectrum matrix based on ST. The decomposition result of the time-varying coherence matrix is relatively smooth, so DPOD interpolation can be introduced to accelerate its decomposition, and the DPOD interpolation technology is extended to the simulation based on measured wind velocity. The numerical experiment has shown that the reconstruction results of coherence matrix interpolation are consistent with the target values, and the interpolation calculation efficiency is higher than that of the coherence matrix time-frequency interpolation method and the coherence matrix POD interpolation method. Compared to existing data-driven simulation methods, it addresses the efficiency issue in simulations where the number of Cholesky decompositions increases with the increase of simulation points, significantly enhancing the efficiency of simulating multivariate non-stationary wind velocities. Meanwhile, the simulation data preserved the time-frequency characteristics of the measured wind velocity well.

• Composites Research
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• v.36 no.5
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• pp.329-334
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• 2023

The classical multiscale finite element (FE² ) method involves iterative calculations of micro-boundary value problems for representative volume elements at every integration point in macro scale, making it a computationally time and data storage space. To overcome this, we developed the data-driven multiscale analysis method based on the mean-field homogenization (MFH). Data-driven computational mechanics (DDCM) analysis is a model-free approach that directly utilizes strain-stress datasets. For performing multiscale analysis, we efficiently construct a strain-stress database for the microstructure of composite materials using mean-field homogenization and conduct data-driven computational mechanics simulations based on this database. In this paper, we apply the developed multiscale analysis framework to an example, confirming the results of data-driven computational mechanics simulations considering the microstructure of a hyperelastic composite material. Therefore, the application of data-driven computational mechanics approach in multiscale analysis can be applied to various materials and structures, opening up new possibilities for multiscale analysis research and applications.

• Journal of Biomedical Engineering Research
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• v.35 no.5
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• pp.142-150
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• 2014

The key to model-based iterative reconstruction (MBIR) algorithms for transmission computed tomography lies in the ability to accurately model the data formation process from the emitted photons produced in the transmission source to the measured photons at the detector. Therefore, accurately modeling the system matrix that accounts for the data formation process is a prerequisite for MBIR-based algorithms. In this work we compared quantitative performance of the three representative ray-driven methods for calculating the system matrix; the ray-tracing method (RTM), the distance-driven method (DDM), and the strip-area based method (SAM). We implemented the ordered-subsets separable surrogates (OS-SPS) algorithm using the three different models and performed simulation studies using a digital phantom. Our experimental results show that, in spite of the more advanced features in the SAM and DDM, the traditional RTM implemented in the OS-SPS algorithm with an edge-preserving regularizer out-performs the SAM and DDM in restoring complex edges in the underlying object. The performance of the RTM in smooth regions was also comparable to that of the SAM or DDM.

• ETRI Journal
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• v.38 no.2
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• pp.337-346
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• 2016

We propose a data-driven kinematic control method for a robotic spatial augmented reality (RSAR) system. We assume a scenario where a robotic device and a projector-camera unit (PCU) are assembled in an ad hoc manner with loose kinematic specifications, which hinders the application of a conventional kinematic control method based on the exact link and joint specifications. In the proposed method, the kinematic relation between a PCU and joints is represented as a set of B-spline surfaces based on sample data rather than analytic or differential equations. The sampling process, which automatically records the values of joint angles and the corresponding external parameters of a PCU, is performed as an off-line process when an RSAR system is installed. In an on-line process, an external parameter of a PCU at a certain joint configuration, which is directly readable from motors, can be computed by evaluating the pre-built B-spline surfaces. We provide details of the proposed method and validate the model through a comparison with an analytic RSAR model with synthetic noises to simulate assembly errors.

• KIEAE Journal
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• v.17 no.5
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• pp.77-86
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• 2017

Purpose: Because of the growing concern over fossil fuel use and increasing demand for greenhouse gas emission reduction since the 1990s, the building energy analysis field has produced various types of methods, which are being applied more often and broadly than ever. A lot of research products have been actively proposed in the area of the building energy simulation for over 50 years around the world. However, in the last 20 years, there have been only a few research cases where the trend of building energy analysis is examined, estimated or compared. This research aims to investigate a trend of the building energy analysis by focusing on methodology and characteristics of each method. Method: The research papers addressing the building energy analysis are classified into two types of method: engineering analysis and algorithm estimation. Especially, EPG(Energy Performance Gap), which is the limit both for the existing engineering method and the single algorithm-based estimation method, results from comparing data of two different levels- in other words, real time data and simulation data. Result: When one or more ensemble algorithms are used, more accurate estimations of energy consumption and performance are produced, and thereby improving the problem of energy performance gap.

• Structural Monitoring and Maintenance
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• v.6 no.2
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• pp.125-145
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• 2019

Bridge scour is one of the predominant causes of bridge failure. Current climate deterioration leads to increase of flooding frequency and severity and thus poses a higher risk of bridge scour failure than before. Recent studies have explored extensively the vibration-based scour monitoring technique by analyzing the structural modal properties before and after damage. However, the state-of-art of this area lacks a systematic approach with sufficient robustness and credibility for practical decision making. This paper attempts to develop a data-driven methodology for bridge scour monitoring using support vector machines. This study extracts features from the bridge dynamic responses based on a generic sensitivity study on the bridge's modal properties and selects the features that are significantly contributive to bridge scour detection. Results indicate that the proposed data-driven method can quantify the bridge scour damage with satisfactory accuracy for most cases. This paper provides an alternative methodology for bridge scour evaluation using the machine learning method. It has the potential to be practically applied for bridge safety assessment in case that scour happens.

• Journal of the Korea Society for Simulation
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• v.33 no.3
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• pp.1-11
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• 2024

There are various methods to evaluate performance of a internal combustion engine. However, there have been several limitations for each methods. In this study, to overcome such limitations of a previous method, a data-driven model and physics based model linked simulation model is developed. The representative components of turbocharged engine which participate in running loop of an engine are sorted out. Sorted out components are modeled either by data-driven method or by physics based method. The engine simulator is developed by combining component model using C++ and Python. The convergence of several variables is tested to verify a simulator. Finally, as most variables has shown less than 5% error in comparison between the simulation result and the real engine test result, it is concluded that the simulator is validated. It is expected that the developed simulator could evaluate performance of various engine models with small effort. In addition, it is expected that the developed simulator would play a key role in developing an engine digital twin.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.3
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• pp.85-90
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• 2015

In this paper, anew parallel event-driven logic simulation is proposed. As the proposed prediction-based parallel event-driven simulation method uses both prediction data and actual data for the input and output values of local simulations executed in parallel, the synchronization overhead and the communication overhead, the major bottleneck of the performance improvement, are greatly reduced. Through the experimentation with multiple designs, we have observed the effectiveness of the proposed approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2831-2845
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• 2022

Compared to the significant approach of project-based learning research, a data-driven design project-based learning has not reached a meaningful consensus regarding the most valid and reliable method for assessing design creativity. This article proposes an advanced information data-interactive learning system for creative design using a service design process that combines a design thinking. We propose a service framework to improve the convergence design process between students and advanced information data analysis, allowing students to participate actively in the data visualization and research using patent data. Solving a design problem by discovery and interpretation process, the Advanced information-interactive learning framework allows the students to verify the creative idea values or to ideate new factors and the associated various feasible solutions. The student can perform the patent data according to a business intelligence platform. Most of the new ideas for solving design projects are evaluated through complete patent data analysis and visualization in the beginning of the service design process. In this article, we propose to adapt advanced information data to educate the service design process, allowing the students to evaluate their own idea and define the problems iteratively until satisfaction. Quantitative evaluation results have shown that the advanced information data-driven learning system approach can improve the design project - based learning results in terms of design creativity. Our findings can contribute to data-driven project-based learning for advanced information data that play a crucial role in convergence design in related standards and other smart educational fields that are linked.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1095-1100
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• 2011

In this paper, PHM (Prognostics and Health Management) techniques are briefly outlined. Prognostics, being a central step within the PHM, is explained in more detail, stating that there are three approaches - experience based, data-driven and model based approaches. Representative articles in the field of prognostics are also given in terms of the type of faults. Model based method is illustrated by introducing a case study that was conducted to the crack growth of the gear plate in UH-60A helicopter. The paper also addresses the comparison of the OBM (Overall Bayesian Method), which was developed by the authors with the PF (Particle Filtering) method, which draws great attention recently in prognostics, through the study on a simple crack growth problem. Their performances are examined by evaluating the metrics introduced by PHM society.