• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.611-620
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• 2020

Although process-based models have been a preferred approach for modeling freshwater aquatic systems over extended time intervals, the increasing utility of data-driven models in a big data environment has made the data-driven models increasingly popular in recent decades. In this study, international peer-reviewed journals for the relevant fields were searched in the Web of Science Core Collection, and an extensive literature review, which included total 2,984 articles published during the last two decades (2000-2020), was performed. The review results indicated that the rate of increase in the number of published studies using data-driven models exceeded those using process-based models since 2010. The increase in the use of data-driven models was partly attributable to the increasing availability of data from new data sources, e.g., remotely sensed hyperspectral or multispectral data. Consistently throughout the past two decades, South Korea has been one of the top ten countries in which the greatest number of studies using the data-driven models were published. Among the major data-driven approaches, i.e., artificial neural network, decision tree, and Bayesian model, were illustrated with case studies. Based on the review, this study aimed to inform the current state of knowledge regarding the biogeochemical water quality and ecological models using data-driven approaches, and provide the remaining challenges and future prospects.

• 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.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.565-568
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• 2000

본 논문에서는 data-driven 반향제거기 구조에서 타이밍 지터의 보상 방법을 제안한다. V.90PCM 모뎀환경에서 네트윅 클록에 동기가 되어 동작하는 사용자 터미널 모뎀이 디지털 PLL (DPLL)을 이용하여 타이밍 복원을 하면 타이밍 지터 성분이 반향제거기의 성능을 순간적으로 악화 시키게 된다. 제안된 방법은 두개의 계수세트 들로부터 타이밍 지터 발생시 필요한 계수를 디콘볼루션 알고리듬을 이용하여 FIR 필터링을 통해 구하며 발생하는 지터 성분 의 대부분을 보상 해 준다. 또한 제안 방법은 waveform driven 반향제거기에 비해 약간의 성능열화가 있지만 적은 연산량으로 타이밍 지터보상을 할 수 있는 장점이 있다.

• 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.

• 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.

• Smart Media Journal
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• v.11 no.10
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• pp.54-64
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• 2022

A big data-driven digital transformation is defined as a process that aims to innovate companies by triggering significant changes to their capabilities and designs through the use of big data and various technologies. For a successful big data-driven digital transformation, reviewing related literature, which enhances the understanding of research statuses and the identification of key research topics and relationships among key topics, is necessary. However, understanding and describing literature is challenging, considering its volume and variety. Establishing a common ground for central concepts is essential for science. To clarify key research topics on the big data-driven digital transformation, we carry out a comprehensive literature review by performing text mining of 439 articles. Text mining is applied to learn and identify specific topics, and the suggested key references are manually reviewed to develop a state-of-the-art overview. A total of 10 key research topics and relationships among the topics are identified. This study contributes to clarifying a systematized view of dispersed studies on big data-driven digital transformation across multiple disciplines and encourages further academic discussions and industrial transformation.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.123-130
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• 2018

With the emergence of a new paradigm called Open Science and Big Data, the need for data sharing and collaboration is also emerging in the computational science field. This paper, we analyzed data-driven research cases for computational science by field; material design, bioinformatics, high energy physics. We also studied the characteristics of the computational science data and the data management issues. To manage computational science data effectively it is required to have data quality management, increased data reliability, flexibility to support a variety of data types, and tools for analysis and linkage to the computing infrastructure. In addition, we analyzed trends of platform technology for efficient sharing and management of computational science data. The main contribution of this paper is to review the various computational science data repositories and related platform technologies to analyze the characteristics of computational science data and the problems of data management, and to present design considerations for building a future computational science data platform.

• Smart Structures and Systems
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• v.5 no.4
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• pp.415-426
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• 2009

This study focuses on the concept of multi-scale wireless sensor networks for damage detection in civil infrastructure systems by first over viewing the general network philosophy and attributes in the areas of data acquisition, data reduction, assessment and decision making. The data acquisition aspect includes a scalable wireless sensor network acquiring acceleration and strain data, triggered using a Restricted Input Network Activation scheme (RINAS) that extends network lifetime and reduces the size of the requisite undamaged reference pool. Major emphasis is given in this study to data reduction and assessment aspects that enable a decentralized approach operating within the hardware and power constraints of wireless sensor networks to avoid issues associated with packet loss, synchronization and latency. After over viewing various models for data reduction, the concept of a data-driven Bivariate Regressive Adaptive INdex (BRAIN) for damage detection is presented. Subsequent examples using experimental and simulated data verify two major hypotheses related to the BRAIN concept: (i) data-driven damage metrics are more robust and reliable than their counterparts and (ii) the use of heterogeneous sensing enhances overall detection capability of such data-driven damage metrics.

• Information and Communications Magazine
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• v.5 no.2
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• pp.245-256
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• 1988

본 논문에서는 적응 데이터 반향제거기(adaptive data-driven echo canceller)의 여러 구조에 대한 성능을 고찰한다. 우선 각 data-driven echo canceller (DDEC)의 시스템식을 일관성 있는 기준에 의하여 분류하고 연관성을 살펴본다. 특히, 본 논문에서 제안된 새로운 DDEC 알고리즘의 기존의 방법과의 차이점을 중점 기술한다. 또한 각 DDEC구조의 성능 및 복잡성을 비교 분석한다. 결과로서, 제안된 새로운 알고리즘이 성능이 우수할 뿐만아니라 복잡성 또한 적음을 입증하였으며 이를 시뮬레이션을 통하여 확인하였다.

• 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.