• Environmental Engineering Research
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• v.10 no.2
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• pp.88-103
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• 2005

In this paper, data-driven modeling and multiresolution analysis (MRA) are applied for a full-scale wastewater treatment plant (WWTP). The proposed method is based on modeling by partial least squares (PLS) and multiscale monitoring by a generic dissimilarity measure (GDM), which is suitable for nonstationary and non-normal process monitoring such as a biological process. Case study in an industrial plant showed that the PLS model could give good modeling performance and analyze the dynamics of a complex plant and MRA was useful to detect and isolate various faults due to its multiscale nature. The proposed method enables us to show the underlying phenomena as well as to filter out unwanted and disturbing phenomena.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.97-104
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• 2016

In this paper, we propose spatial information based simulator for user experience optimization and minimize real space complexity. We focus on developing simulator how to design virtual space model and to implement virtual character using real space data. Especially, we use expanded events-driven inference model for SVM based on machine learning. Our simulator is capable of feature selection by k-fold cross validation method for optimization of data learning. This strategy efficiently throughput of executing inference of user behavior feature by virtual space model. Thus, we aim to develop the user experience optimization system for people to facilitate mapping as the first step toward to daily life data inference. Methodologically, we focus on user behavior and space modeling for implement virtual space.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.12
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• pp.2061-2071
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• 2003

The macro-parametric approach, which is a method of CAD model exchange, has recently been proposed. CAD models can be exchanged in the form of a macro file, which is a sequence of modeling commands. As an event-driven commands set, the standard macro file can transfer design intents such as parameters, features and constraints. Moreover it is suitable for the network environment because the standard macro commands are open, explicit, and the data size is small. This paper introduces the concept of the macro-parametric method and proposes its representation using XML technology. Representing the macro-parametric data using XML allows managing vast amount of dynamic contents, Web-enabled distributed applications, and inherent characteristic of structure and validation.

• Computers and Concrete
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• v.32 no.6
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• pp.625-637
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• 2023

The application of ML approaches in determining the resisting capacity of fire damaged RC columns is introduced in this paper, on the basis of analysis data driven ML modeling. Considering the characteristics of the structural behavior of fire damaged RC columns, the representative five approaches of Kernel SVM, ANN, RF, XGB and LGBM are adopted and applied. Additional partial monotonic constraints are adopted in modelling, to ensure the monotone decrease of resisting capacity in RC column with fire exposure time. Furthermore, additional suggestions are also added to mitigate the heterogeneous composition of the training data. Since the use of ML approaches will significantly reduce the computation time in determining the resisting capacity of fire damaged RC columns, which requires many complex solution procedures from the heat transfer analysis to the rigorous nonlinear analyses and their repetition with time, the introduced ML approach can more effectively be used in large complex structures with many RC members. Because of the very small amount of experimental data, the training data are analytically determined from a heat transfer analysis and a subsequent nonlinear finite element (FE) analysis, and their accuracy was previously verified through a correlation study between the numerical results and experimental data. The results obtained from the application of ML approaches show that the resisting capacity of fire damaged RC columns can effectively be predicted by ML approaches.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.621-635
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• 2020

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.

• Nuclear Engineering and Technology
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• v.44 no.8
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• pp.889-896
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• 2012

Recent progress in the computational fluid dynamics methods of two- and multiphase phase flows has already started opening up new exciting possibilities for using complete multidimensional models to simulate boiling systems. Combining this new theoretical and computational approach with novel experimental methods should dramatically improve both our understanding of the physics of boiling and the predictive capabilities of models at various scale levels. However, for the multidimensional modeling framework to become an effective predictive tool, it must be complemented with accurate mechanistic closure laws of local boiling mechanisms. Boiling heat transfer has been studied quite extensively before. However, it turns out that the prevailing approach to the analysis of experimental data for both pool boiling and forced-convection boiling has been associated with formulating correlations which normally included several adjustable coefficients rather than based on first principle models of the underlying physical phenomena. One reason for this has been the tendency (driven by practical applications and industrial needs) to formulate single expressions which encompass a broad range of conditions and fluids. This, in turn, makes it difficult to identify various specific factors which can be independently modeled for different situations. The objective of this paper is to present a mechanistic modeling concept for both pool boiling and forced-convection boiling. The proposed approach is based on theoretical first-principle concepts, and uses a minimal number of coefficients which require calibration against experimental data. The proposed models have been validated against experimental data for water and parametrically tested. Model predictions are shown for a broad range of conditions.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.37-38
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• 2018

BIM(Building Information Modeling) in the architecture, VDC(Virtual Design and Construction) defined CIFE(Center for Integrated Facility Engineering) of Stanford university in USA, and Data-driven design definition issued by TECDOC-1284 of IAEA are doing data-level design generated by 3D CAD technology, integrating and managing related information based on the 3D model, and Using 3D models effectively during nuclear power plant life cycle. 3D model of domestic nuclear power industry is using interference review between design fields, 4D system linked 3D construction model and schedule activity, but the 3D model generated in the design phase is effectively not utilized during the construction, operation, decommissioning. therefore, This study is aimed to suggest 3D model LOD(Level of Detail) advancement method through the analysis of existing literature, 2D drawings, and 3D models throughout nuclear power plant lifecycle.

• Speech Sciences
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• v.10 no.1
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• pp.71-84
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• 2003

In the acoustic modeling for large vocabulary speech recognition, a sparse data problem caused by a huge number of context-dependent (CD) models usually leads the estimated models to being unreliable. In this paper, we develop a new clustering method based on the C45 decision-tree learning algorithm that effectively encapsulates the CD modeling. The proposed scheme essentially constructs a supervised decision rule and applies over the pre-clustered triphones using the C45 algorithm, which is known to effectively search through the attributes of the training instances and extract the attribute that best separates the given examples. In particular, the data driven method is used as a clustering algorithm while its result is used as the learning target of the C45 algorithm. This scheme has been shown to be effective particularly over the database of low unknown-context ratio in terms of recognition performance. For speaker-independent, task-independent continuous speech recognition task, the proposed method reduced the percent accuracy WER by 3.93% compared to the existing rule-based methods.

• Nuclear Engineering and Technology
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• v.30 no.6
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• pp.496-506
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• 1998

This paper deals with the computational modeling of buoyancy-driven turbulent heat transfer involving spatially uniform volumetric heat sources in semicircular geometry. The Launder & Sharma low-Reynolds number k-$\varepsilon$ turbulence model without any modifications and the SIMPLER computational algorithm were used for the numerical modeling, which was incorporated into the new computer code CORE-TNC. This computer code was subsequently benchmarked with the Mini-ACOPO experimental data in the modified Rayleigh number range of 2$\times$10$^{13}$ $\times$10$^{14}$ . The general trends of the velocity and temperature fields were well predicted by the model used, and the calculated isotherm patterns were found to be very similiar to those observed in previous experimental investigations. The deviation between the Mini-ACOPO experimental data and the corresponding numerical results obtained with CORE-TNC for the average Nusselt number was less than 30% using fine grid in the near-wall region and the three-point difference formula for the wall temperature gradient. With isothermal pool boundaries, heat was convected predominantly to the upper and adjacent lateral surfaces, and the bottom surface received smaller heat fluxes.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.294-298
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• 2015

In the United States, the completion of Construction Work Zone (CWZ) impact assessments for all federally-funded highway infrastructure improvement projects is mandated, yet it is regarded as a daunting task for state transportation agencies, due to a lack of standardized analytical methods for developing sounder Transportation Management Plans (TMPs). To circumvent these issues, this study aims to create a spatiotemporal modeling framework, dubbed "SWAT" (Spatiotemporal Work zone Assessment for TMPs). This study drew a total of 43,795 traffic sensor reading data collected from heavily trafficked highways in U.S. metropolitan areas. A multilevel-cluster-driven analysis characterized traffic patterns, while being verified using a measurement system analysis. An artificial neural networks model was created to predict potential 24/7 traffic demand automatically, and its predictive power was statistically validated. It is proposed that the predicted traffic patterns will be then incorporated into a what-if scenario analysis that evaluates the impact of numerous alternative construction plans. This study will yield a breakthrough in automating CWZ impact assessments with the first view of a systematic estimation method.