• 한국물환경학회지
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• 제36권6호
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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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• 제36권5호
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• pp.329-334
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• 2023

기존의 멀티스케일 유한요소법(Multiscale finite element, FE² )은 거시 스케일의 모든 적분점에서 대표 체적요소(representative volume element, RVE)의 미시 경계치 문제를 반복적으로 계산하기 때문에 긴 해석 시간과 많은 데이터 저장 공간을 필요로 한다. 이를 해결하기 위해 본 연구에서 평균장 균질화 데이터 기반 멀티스케일 해석 기법을 개발하였다. 데이터 기반 전산역학(data-driven computational mechanics, DDCM) 해석은 변형률-응력 데이터 셋을 직접적으로 사용하는 모델-프리(model-free)접근 방식이다. 멀티스케일 해석을 수행하기 위해, 평균장 균질화(mean-field homogenization)를 활용하여 복합재의 미세구조에 대한 변형률-응력 데이터베이스(database)를 효율적으로 구축하고, 이를 기반으로 데이터 기반 전산역학 시뮬레이션을 수행하였다. 본 논문에서는 개발한 멀티 스케일 해석 프레임워크(framework)를 예제에 적용하여, 초탄성(hyperelasticity) 복합재의 미세 구조를 고려한 데이터 기반 전산역학 시뮬레이션 결과를 확인하였다. 따라서, 데이터 기반 전산역학 접근 방식을 활용한 멀티스케일 해석기법은 다양한 재료 및 구조에 적용될 수 있으며, 멀티스케일 해석 연구 및 응용 가능성을 열어줄 것으로 기대된다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 제13회 신호처리 합동 학술대회 논문집
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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.

• 한국IT서비스학회지
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• 제17권1호
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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.

• Wind and Structures
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• 제39권2호
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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.

• 정보처리학회논문지D
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• 제15D권5호
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• pp.681-690
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• 2008

단백질체학에서 특정 조건 하에서 단백질의 기능 이상 및 구조 변형 유무를 규명하고 질병 과정을 추적하는 것은 중요한 연구이다. 일반적으로 단백질의 발현량 변화 분석에는 통계적 방법이 많이 사용되고 있으며 단백질 상용 이미지 분석 소프트웨어에서 제공하는 그래픽을 이용한 방법들도 있으나, 이 방법들은 많은 조직 내에 존재하는 수많은 단백질을 수동으로 비교해야 하는 어려움이 있다. 본 논문에서는 데이터베이스와 데이터마이닝 기법을 이용하여 OLAP 데이터 큐브와 Discovery-driven 탐색의 응용 방법을 제안한다. 데이터 큐브의 특성을 이용함에 의해서, 질병에 의해 발현량이 변하는 단백질 뿐 아니라 임상적 특성과 단백질의 영향 관계를 분석하는 것이 가능하다. 데이터 큐브에서 단백질의 발현량 변화 분석에 적합한 데이터 큐브의 척도와Discovery-driven 탐색을 위한 예외 지표를 제안하고, 특히 In-exception을 계산하는데 있어서의 계산량 감소 방안을 제시한다. 실험을 통해 폐암 2-DE 데이터에서 데이터 큐브와 Discovery-driven 방법이 유용함을 보인다.

• 아태비즈니스연구
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• 제15권1호
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• pp.131-143
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• 2024

Purpose - The purpose of this study was to analyze cases of big data-driven business in the financial industry, focusing on organizational structure and business processes using big data in banking industry. Design/methodology/approach - This study used a case study approach. To this end, cases of two banks implementing big data-driven business were collected and analyzed. Findings - There are two things in common between the two cases. One is that the central tasks for big data-driven business are performed by a centralized organization. The other is that the role distribution and work collaboration between the headquarters and business departments are well established. On the other hand, there are two differences between the two banks. One marketing campaign is led by the headquarters and the other marketing campaign is led by the business departments. The two banks differ in how they carry out marketing campaigns and how they carry out big data-related tasks. Research implications or Originality - When banks plan and implement big data-driven business, the common aspects of the two banks analyzed through this case study can be fully referenced when creating an organization and process. In addition, it will be necessary to create an organizational structure and work process that best fit the special situation considering the company's environment or capabilities.

• 스마트미디어저널
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• 제11권10호
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• pp.54-64
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• 2022

빅데이터 기반의 디지털 트랜스포메이션은 데이터 및 데이터 관련 기술을 통해 기업의 성과 향상, 조직 변화, 사회 공헌 등의 목적 달성을 위해 수행하는 혁신적 프로세스를 의미한다. 성공적인 빅데이터 기반의 디지털 트랜스포메이션을 위해서는 관련 연구 현황, 주요 연구토픽, 주요 연구토픽 간의 관계를 이해하는 것이 필수적이다. 그러나 여러 연구들의 서로 다른 관점 및 이들 간 연계 가능성에 대해 이해하려는 노력은 아직 미진하다. 본 논문은 텍스트마이닝을 활용하여 관련 연구동향을 분석하고, 여러 연구의 다양한 관점을 통합적으로 이해하기 위한 기반 마련을 시도해보았다. Web of Science Core Collection에서 추출한 439편의 논문을 분석하여, 10개의 주요 연구토픽을 도출하였고, 이들 간의 관계를 분석하였다. 본 연구의 결과가 빅데이터 기반의 디지털 트랜스포메이션에 대한 통합적인 이해를 촉진하고, 성공을 위한 방향성 모색에 기여할 것으로 기대한다.

• 인터넷정보학회논문지
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• 제19권1호
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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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• 제5권4호
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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.