• ETRI Journal
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• 제38권5호
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• pp.1019-1029
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• 2016

The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target-tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement-driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre-filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement-driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

• 한국지반공학회논문집
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• 제19권4호
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• pp.235-246
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• 2003

국내의 경우 이 등(1994)의 연구를 시작으로 국지 현장별로 타입말뚝의 지지력 증가효과에 대한 사례연구들이 있었다. 그러나 충분한 시험자료를 바탕으로 한 지지력 증가효과에 대한 전반적인 검토는 아직 미흡한 실정이고, 또한 지지력 증가효과에 지반조건 및 말뚝의 형상이 미치는 영향에 대해서는 아직 보고 된 바 없다. 타입말뚝의 지지력 증가효과를 실무적으로 이용하기 위해서는 현장에서 실구조물에 적용된 말뚝에 대한 시험자료의 분석을 실시하는 것이 필요하다. 본 연구에서는 말뚝기초의 설계 및 향후 사질토에서의 지지력 증가효과 등의 연구를 위한 기본 자료로 활용하기 위해 실구조물에 적용되는 다양한 시험말뚝의 시공과 동재하시험 자료 분석을 통해 지지력 증가효과에 대한 현상을 알아보았다. 말뚝의 동재하시험의 분석 결과, 말뚝지지력의 증가는 토질별로도 영향을 받지만 조성이력에도 영향을 받는 것으로 분석되었으며, 특히 사질토에도 상당한 지지력 증가효과가 나타났다. 이러한 말뚝의 지지력의 증가는 주로 주면부에서 발생하였으며, 선단형상에 따른 지지력 증가효과는 점성토에서는 폐단말뚝이 크고, 사질토에서는 개단말뚝이 큰 것으로 나타났다.

• Ocean and Polar Research
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• 제31권1호
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• pp.11-29
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• 2009

Variability of sea surface temperature (SST) in the Japan/East Sea (JES) was studied using complex empirical orthogonal function (CEOF) analysis. Two daily data sets were analyzed: (1) New Generation 0.05o-gridded SST from Tohoku University, Japan (July 2002-July 2006), and (2) 0.25o-gridded SST from the Japan Meteorological Agency (October 1993-November 2006). Linkages with wind stress curl were revealed using 6-h 1o-gridded surface zonal and meridional winds from ancillary data of the Sea- WiFS Project, a special National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) product (1998-2005). SST anomalies (SSTA) were obtained by removing the seasonal signal, estimated as the leading mode of the CEOF decomposition of the original SST. Leading CEOF modes of residual SSTA obtained from both data sets were consistent with each other and were characterized by annual, semiannual, and quasi-biennial time scales estimated with 95% statistical significance. The Semiannual Mode lagged 2 months behind the increased occurrence of the anticyclonic (AC) wind stress curl over the JES. Links to dynamic processes were investigated by numerical simulations using an oceanic model. The suggested dynamic forcings of SSTA are the inflow of subtropical water into the JES through the Korea Strait, divergence in the surface layer induced by Ekman suction, meridional shifts of the Subarctic Front in the western JES, AC eddy formation, and wind-driven strengthening/weakening of large-scale currents. Events of west-east SSTA movement were identified in July-September. The SSTA moved from the northeastern JES towards the continental coast along the path of the westward branch of the Tsushima Current at a speed consistent with the advective scale.

• International Journal of Knowledge Content Development & Technology
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• 제14권3호
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• pp.77-91
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• 2024

This article aims to comprehensively analyze the crucial role played by data analytics and business intelligence (BI) strategies in propelling digital transformation within diverse industries. Through an extensive literature review and examination of real-world case studies, the study employs a systematic analysis of scholarly works and industry reports. This approach provides a panoramic view of how organizations utilize data-driven insights for competitive advantages, improved customer experiences, and fostering innovation. The findings underscore the pivotal significance of data analytics and BI strategies in influencing strategic decision-making, enhancing operational efficiency, and ensuring long-term sustainability across various industries. The study stands out in its originality by offering a unique synthesis of insights derived from scholarly works and real-world case studies, contributing to a holistic understanding of the transformative impact of data analytics and BI on contemporary business practices. While the study provides valuable insights, limitations include the scope of available literature and case studies. The implications call for further research to explore emerging trends and evolving challenges in the dynamic landscape of data analytics and BI. The practical implications highlight the tangible benefits organizations can derive from integrating data analytics and BI strategies, emphasizing their role in shaping strategic decisions and fostering operational efficiency. In a broader context, the study delves into the social implications of the symbiotic relationship between data analytics, BI, and digital transformation. It explores how these strategies impact broader societal and economic aspects, influencing innovation and sustainability.

• Advances in Energy Research
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• 제6권2호
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• pp.91-101
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• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• 서비스연구
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• 제6권2호
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• pp.117-131
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• 2016

본 연구는 국내 외식서비스기업 종사원을 대상으로 개인이 지각하는 경력정체를 구조적 정체와 내용적 정체로 구분하여 이직의도에 미치는 영향을 실증적으로 분석하고 경력동기와 조직경력개발지원이 경력정체와 상호작용하면서 이직의도에 미치는 영향을 구명하고자 하였다. 본 연구를 수행하기 위해 총 350명의 외식서비스기업종사자들의 설문조사를 실시하였고, 최종 255명의 자료를 분석에 활용하여 연구가설을 실증적으로 검증하였다. 분석 결과, 종업원이 지각하는 구조적 경력정체와 내용적 경력정체 모두 이직의도에 정(+)의 영향을 미치는 것으로 나타났다. 또한 경력정체와 이직의도간 관계에서 공식적 지원과 비공식적 지원의 조절효과 검증결과, 비공식적 지원의 조절효과가 확인되었다. 본 연구를 통해서 외식서비스 기업 종사원이 지각하는 구조적, 내용적 경력정체는 이직의도에 영향을 미치는 중요한 변인이지만 기업의 비공식적 지원은 이직의도를 조절할 수 있으므로, 기업차원에서 조직 구성원의 이직의도를 줄이기 위해 이를 활용하는 방법을 적용할 수 있을 것이다.

• 아태비즈니스연구
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• 제13권1호
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• pp.37-52
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• 2022

Purpose - The objective of this study is to investigate what motivates global FDI inflows in the different economic development level and to clarify the FDI motivation type in the level of qualitative economic growth. Design/methodology/approach - Major macroscopic social·economic factors induced FDI inflows were analyzed using fixed-effect panel regression with 30-year panel data of 28 countries from 1985 to 2014. For analysis in the stage of economic growth, two category of developed and developing countries was used. And to analyze FDI motivation type in the level of qualitative economic growth, 4 shares of GDP; consumption·government·investment expenditure and export, was used as explanatory variable. Findings - In developed country, TFP(total factor productivity) and GDP have a great influence on FDI inflows, and consumption and labor compensation have a slight effect. This result indicates that the market seeking-driven, horizontal type investment is shown along with efficiency seeking investment. In developing country, human capital and TFP is shown to have greater impact on FDI inflows and labor compensation, exports, investment and government expenditures also have impacts. Thus it has confirmed that not only efficiency-seeking vertical investment for using low cost well educated laborer, but also government-driven economic growth and export policies could affect the FDI inflows. Research implications or Originality - The FDI investment decision making of multinational companies is decided by their own purpose. But, in the concept of as follows; 1) FDI is a long-term capital flowing for maximization of economic utility with limited global resource, 2) Thus FDI could be affected by macro socio·economic factors of host country. 3) Also such macro factors is different by each economic growth qualitative level. Therefore macro socio·economic factors of each country could be affected by the qualitative level of their own economic growth. To attract FDI inflows, it is desirable to implement differentiated incentive policies in the qualitative level of economic growth. Furthermore in developing countries it is recommended to implement government driven economic growth policies as follows; fostering well educated human resources, improving technology productivity in the relative lower cost labor market compared to developed countries and boosting international export volume.

• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.54-61
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• 2023

The family tree is one of the key elements of humanities classics research and is very important for accurately understanding people or families. In this paper, we introduce a method for automatically generating a family tree using information on interpersonal relationships (IIPR) from the Korean Classics Database (KCDB) and visualize interpersonal searches within a family tree using data-driven document JavaScript (d3.js). To date, researchers of humanities classics have wasted considerable time manually drawing family trees to understand people's influence relationships. An automatic family tree builder analyzes a database that visually expresses the desired family tree. Because a family tree contains a large amount of data, we analyze the performance and bottlenecks according to the amount of data for visualization and propose an optimal way to construct a family tree. To this end, we create an n-ary tree with fake data, visualize it, and analyze its performance using simulation results.

• 한국산업정보학회논문지
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• 제17권4호
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• pp.71-85
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• 2012

본 논문은 운항중인 선박에서 기록되어지는 운항정보 및 엔진 가동정보 등을 실시간으로 모니터링하고, 문제 발생 시에 그 근본원인을 찾아내어 민첩하게 대응할 수 있는 일련의 결함원인 분석 및 예방시스템 개발을 목적으로 한다. 결함분석을 위해서는 선박엔진의 주요기관에 부착된 센서들로부터 장기간 수집된 정보를 사용하게 되는데, 이 양이 매우 방대하며, 잡음 및 중복정보(Redundancy)가 너무 많이 포함되어, 수집된 센서 데이터를 바로 고장분석에 사용하기에는 어려움이 있다. 따라서 본 논문에서는 방대한 양의 데이터 중, 정보의 손실을 최소화하고 중요한 정보만을 추출하기 위해 'Equal-frequency binning'과 'Entropy' 기반의 데이터 필터링 방법에 관해 연구하였다. 실제로 시험운용 중인 선박엔진 데이터를 개발된 선박엔진 고장분석 소프트웨어를 이용하여 결함분석을 수행하여, 제안된 방법의 효용성을 검증한다.

• 한국수소및신에너지학회논문집
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• 제34권5호
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• pp.478-489
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• 2023

In this study, fault diagnosis and detection methods developed to ensure the reliability of polymer electrolyte membrane (PEM) hydrogen electrolysis systems have been proposed. The proposed method consists of model development and data generation of the PEM hydrogen electrolysis system, and data-driven fault diagnosis learning model development. The developed fault diagnosis learning model describes how to detect and classify faults in the sensors and components of the system.