• 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.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.7 s.262
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• pp.747-755
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• 2007

To improve the reality of motion simulator, the method of data-driven motion generation has been introduced to simply record and replay the motion of real vehicles. We can achieve high quality of reality from real samples, but it has no interactions between users and simulations. However, in character animation, user controllable motions are generated by the database made up of motion capture signals and appropriate control algorithms. In this study, as a tool for the interactive data-driven driving simulator, we proposed a new motion generation method. We sample the motion data from a real vehicle, transform the data into the appropriate data structure(motion block), and store a series of them into a database. While simulation, our system searches and synthesizes optimal motion blocks from database and generates motion stream reflecting current simulation conditions and parameterized user demands. We demonstrate the value of the proposed method through experiments with the integrated motion platform system.

• The KIPS Transactions:PartD
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• v.15D no.5
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• pp.681-690
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• 2008

In proteomics research, the identification of differentially expressed proteins observed under specific conditions is one of key issues. There are several ways to detect the change of a specific protein's expression level such as statistical analysis and graphical visualization. However, it is quiet difficult to handle the spot information of an individual protein manually by these methods, because there are a considerable number of proteins in a tissue sample. In this paper, using database and data mining techniques, the application plan of OLAP data cube and Discovery-driven exploration is proposed. By using data cubes, it is possible to analyze the relationship between proteins and relevant clinical information as well as analyzing the differentially expressed proteins by disease. We propose the measure and exception indicators which are suitable to analyzing protein expression level changes are proposed. In addition, we proposed the reducing method of calculating InExp in Discovery-driven exploration. We also evaluate the utility and effectiveness of the data cube and Discovery-driven exploration in the lung cancer 2-DE gel image.

• 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.

• 한국IT서비스학회:학술대회논문집
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• 2009.11a
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• pp.505-510
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• 2009

전자, 자동차 등 엔지니어링 컨버전스 산업이 발전함에 따라 임베디드 S/W 테스트의 중요성이 증가하고 있다. 그러나, 일반적인 S/W 테스트 방법을 그대로 이용할 경우 임베디드 디바이스의 특성으로 인해 일반적인 품질 수준의 테스트 결과를 얻기 위해 상대적으로 더 많은 비용과 시간을 필요로 하게 된다. 따라서, 다양한 임베디드 시스템의 환경에 적용하기 쉽고, 임베디드 디바이스의 특성에 잘 대응하는 테스트 방법이 요구되는 실정이다. 본 논문에서는 Data-Driven 기법을 이용한 효과적인 임베디드 테스트 자동화 기법을 제안한다.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.155-170
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• 2018

This study examines a data-driven approach for software test automation at an online shopping site. Online shopping sites typically change prices dynamically, offer various discounts or coupons, and provide diverse delivery and payment options such as electronic fund transfer, credit cards, mobile payments (KakaoPay, NaverPay, SyrupPay, ApplePay, SamsungPay, etc.) and so on. As a result, they have to test numerous combinations of possible customer choices continuously and repetitively. The total number of test cases is almost 584 billion. This requires somehow automation of tests in settling payments. However, the record playback approach has difficulties in maintaining automation scripts due to frequent changes and complicated component identification. In contrast, the data-driven approach minimizes changes in scripts and component identification. This study shows that the data-driven approach to test automation is more effective than the traditional record playback method. In 2014 before the test automation, the monthly average defects were 5.6 during the test and 12.5 during operation. In 2015 after the test automation, the monthly average defects were 9.4 during the test and 2.8 during operation. The comparison of live defects and detected errors during the test shows statistically significant differences before and after introducing the test automation using the data-driven approach.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.9
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• pp.113-119
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• 2005

The methods that are used for receiving data from attached devices under real-time preemptive multi-task operating system (OS) by general processors can be categorized as polling and interrupt driven. The technical approach to these methods may be different due to the application specific scheduling policy of the OS and the programming architecture of the flight software. It is one of the most important requirements on the development of the flight software to process the data received from satellite subsystems or components with the exact timeliness and accuracy. This paper presents the analysis of the I/O method of device related scheduling mechanism and the reliable data I/O methods between processor and devices.

• Journal of Digital Contents Society
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• v.8 no.4
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• pp.541-546
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• 2007

Traditional query processing method is not efficient for continuous queries with large continuous stream data. This paper proposes a data-driven query processing method for stream data. The structure of query plan and query execution method are presented. With the proposed method, multiple query processing and sharing among queries can be achieved. Also query execution time can be reduced by storing partial results of query execution. This paper showed an example of query processing with XML data and XQuery query.

• 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.