• 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.

• Nuclear Engineering and Technology
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• v.52 no.6
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• pp.1148-1155
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• 2020

This work presents two different methods for quantifying and propagating the uncertainty associated with fuel composition at end of life for cask criticality calculations. The first approach, the computational approach uses parametric uncertainty including those associated with nuclear data, fuel geometry, material composition, and plant operation to perform forward depletion on Monte-Carlo sampled inputs. These uncertainties are based on experimental and prior experience in criticality safety. The second approach, the data-driven approach relies on using radiochemcial assay data to derive code bias information. The code bias data is used to perturb the isotopic inventory in the data-driven approach. For both approaches, the uncertainty in k_eff for the cask is propagated by performing forward criticality calculations on sampled inputs using the distributions obtained from each approach. It is found that the data driven approach yielded a higher uncertainty than the computational approach by about 500 pcm. An exploration is also done to see if considering correlation between isotopes at end of life affects k_eff uncertainty, and the results demonstrate an effect of about 100 pcm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.235-250
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• 2009

An understanding of performance of database driven network applications is critical in combating slow performance of e-commerce web sites, besides efficient web page design, and high quality and well-maintained web server equipment. This paper describes a method of measuring performance from the user viewpoint, which can help enormously in making realistic assessment of true performance of database driven applications. The performance measurements were taken at user locations by using several specially designed JavaScript functions along with ASP scripts. A performance study is presented in this paper, comparing performance of data access using stored procedures with the traditional way of querying a database. It is generally believed that stored procedures have performance benefits as they are pre-compiled. However, our study shows that the data access approach using stored procedures provides significant benefits(by about 30%) over the traditional approach for querying a commercial MySQL database, only when retrieving a substantial amount of data(at least 10,000 rows of data).

• 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.

• Korean Journal of Computational Design and Engineering
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• v.14 no.2
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• pp.96-104
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• 2009

It gradually becomes important to study on how to efficiently integrate and manage plant lifecycle data such as 2D schematic and 3D solid data, logical configuration data, and equipment specifications data. From this point of view, converting plant design data from various systems into neutral data independent from any commercial systems is one of important technologies for the operation and management of plants which usually have a very long period of life. In order to achieve this goal, a neutral model for efficient integration and management of plant data was defined. After schema mapping between one of specification-driven plant design systems and the neutral model was performed, a plant data translator is also implemented according to the mapping result. Finally, by experiments with nuclear power plant design, the feasibility of the translator was demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.340-367
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• 2018

Storage services integration can be done for achieving high availability, improving data access performance and scalability while preventing vendor lock-in. However, multiple services environment management and interoperability have become a critical issue as a result of service architectures and communication interfaces heterogeneity. Storage federation model provides the integration of multiple heterogeneous and self-sufficient storage systems with a single control point and automated decision making about data distribution. In order to integrate diverse heterogeneous storage services into a single storage pool, we are proposing a storage service federation framework named BoxBroker. Moreover, an automated decision model based on a policy-driven data distribution algorithm and a service evaluation method is proposed enabling BoxBroker to make optimal decisions. Finally, a demonstration of our proposal capabilities is presented and discussed.

• Journal of the Korean Society of Industry Convergence
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• v.19 no.4
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• pp.223-230
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• 2016

This paper presents data-driven thermal protection algorithm for preventing overheating of automotive sunroof motor. When a sunroof motor operates abnormally, its coil is overheated and it is failed. Besides, drivers and passengers are damaged. Hence, the sunroof motor observes its temperature and will be stoped when its temperature reach a predefined level. In order to implement low-cost thermal protection function, we drew a knowledge-based temperature increasing and decreasing curve from the result of experimental test. And then, we implemented data-driven thermal protection algorithm which prevents motor's On/Off operation according to motor operating voltage and motor speed. Finally, we implemented experimental test bed and evaluated its feasibility.

• Advances in nano research
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• v.16 no.3
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• pp.231-249
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• 2024

When the amplitude of the vibrations is equivalent to that clearance, the vibrations for small amplitudes will really be significantly nonlinear. Nonlinearities will not be significant for amplitudes that are rather modest. Finally, nonlinearities will become crucial once again for big amplitudes. Therefore, the concrete panel system may experience a big amplitude in this work as a result of the high temperature. Based on the 3D modeling of the shell theory, the current work shows the influences of the von Kármán strain-displacement kinematic nonlinearity on the constitutive laws of the structure. The system's governing Equations in the nonlinear form are solved using Kronecker and Hadamard products, the discretization of Equations on the space domain, and Duffing-type Equations. Thermo-elasticity Equations. are used to represent the system's temperature. The harmonic solution technique for the displacement domain and the multiple-scale approach for the time domain are both covered in the section on solution procedures for solving nonlinear Equations. An effective data-driven solution is often utilized to predict how different systems would behave. The number of hidden layers and the learning rate are two hyperparameters for the network that are often chosen manually when required. Additionally, the data-driven method is offered for addressing the nonlinear vibration issue in order to reduce the computing cost of the current study. The conclusions of the present study may be validated by contrasting them with those of data-driven solutions and other published articles. The findings show that certain physical and geometrical characteristics have a significant effect on the existing concrete panel structure's susceptibility to temperature change and GPL weight fraction. For building construction industries, several useful recommendations for improving the thermo-mechanics' behavior of structural concrete panels are presented.

• 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.