• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.253-261
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• 2013

In this paper, we study the problem of controlling composite asynchronous sequential machines. The considered asynchronous machine consists of two input/state machines in cascade connection, where the output of the front machine is delivered to the input channel of the rear machine. The objective is to design a corrective controller realizing model matching such that the stable state behavior of the closed-loop system matches that of a reference model. Since the controller receives the state feedback of the rear machine only, there exists uncertainty about the present state of the front machine. We specify the existence condition for a corrective controller given the uncertainty. The design procedure for the proposed controller is described in a case study.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.7
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• pp.354-361
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• 2015

The building energy audit is an important process when collecting basic information for improving the energy performance of existing buildings. Audit parameters should be associated with the energy performance of the building. Such audit parameters will vary according to an individual building's characteristics and energy consumption patterns, but most building energy audits are performed in the same way. The sensitivity analysis (SA) is a statistical method to quantify the correlation between inputs and outputs that can determine which input is influential to which output. Therefore, an SA can identify influential parameters when applied to building energy analysis. In this paper, we adopted the Morris method to identify building energy audit parameters and performed a Monte Carlo simulation for uncertainty analysis. As a result, this method was able to identify an influential parameter for building energy audits and reduce uncertainty in energy consumption in buildings.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3449-3459
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• 2021

With the increasing availability of high-performance computing (HPC) platforms, uncertainty quantification (UQ) and sensitivity analyses (SA) can be efficiently leveraged to optimize design parameters of complex engineering problems using modeling and simulation tools. The workflow involved in such studies heavily relies on HPC resources and hence requires pre-processing and post-processing capabilities of large amounts of data along with remote submission capabilities. The NEAMS Workbench addresses all aspects of the workflows involved in these studies by relying on a user-friendly graphical user interface and a python application program interface. This paper highlights the NEAMS Workbench capabilities by presenting a semiautomated coupling scheme between Dakota and any given package integrated with the NEAMS Workbench, yielding a simplified workflow for users. This new capability is demonstrated by running a SA of a turbulent flow in a pipe using the open-source Nek5000 CFD code. A total of 54 jobs were run on a HPC platform using the remote capabilities of the NEAMS Workbench. The results demonstrate that the semiautomated coupling scheme involving Dakota can be efficiently used for UQ and SA while keeping scripting tasks to a minimum for users. All input and output files used in this work are available in https://code.ornl.gov/neams-workbench/dakota-nek5000-study.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.102-106
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• 2008

This study is to evaluated the impact of varying spatial resolutions of DEM (2 m, 10 m, and 30 m), land use (QuickBird, 1/25,000 and Landsat), and soil data (1/25,000 and 1/50,000) on the uncertainty of Soil and Water Assessment Tool (SWAT) predicted streamflow, sediment, T-N, and T-P transport in a small agricultural watershed ($1.21\;km^2$). SWAT model was adopted and the model was calibrated for a $255.4\;km^2$ watershed using 30 m DEM, Landsat land use, and 1/25,000 soil data. The model was run with the combination of three DEM, land use, and soil map respectively. The SWAT model was calibrated for 2 years (1999-2000) using daily streamflow and monthly water quality (SS, T-N, T-P) records from 1999 to 2000, and verified for another 2 years (2001-2002). The average Nash and Sutcliffe model efficiency was 0.59 for streamflow and the root mean square error were 2.08, 4.30 and 0.70 tons/yr for sediment, T-N and T-P respectively. The hydrological results showed that output uncertainty was biggest by spatial resolution of land use. Streamflow increase the watershed average CN value of QucikBird land use was 0.4 and 1.8 higher than those of 1/25,000 and Landsat land use caused increase of streamflow.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.648-658
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• 2018

Regarding the fact that wind power forecast accuracy is gradually improved as time is approaching, this paper proposes a two-stage rolling dispatch approach based on model predictive control (MPC), which contains an intra-day rolling optimal scheme and a real-time rolling base point tracing scheme. The scheduled output of the intra-day rolling scheme is set as the reference output, and the real-time rolling scheme is based on MPC which includes the leading rolling optimization and lagging feedback correction strategy. On the basis of the latest measured thermal unit output feedback, the closed-loop optimization is formed to correct the power deviation timely, making the unit output smoother, thus reducing the costs of power adjustment and promoting wind power accommodation. We adopt chance constraint to describe forecasts uncertainty. Then for reflecting the increasing prediction precision as well as the power dispatcher's rising expected satisfaction degree with reliable system operation, we set the confidence level of reserve constraints at different timescales as the incremental vector. The expectation of up/down reserve shortage is proposed to assess the adequacy of the upward/downward reserve. The studies executed on the modified IEEE RTS system demonstrate the effectiveness of the proposed approach.

• Asia pacific journal of information systems
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• v.4 no.2
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• pp.35-61
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• 1994

Accounting information system is the most important and formal subsystem of the total information system of an organization which produces and delivers financial information. An organization has its own contigent characteristics. It is the key to a successful development of accounting information system to select the development mode and information characteristics appropriate for the contigent characteristics. The main purpose of this study is to analyze the relationship between the contigent factors and the development mode of accounting information system. In addition, this study will examine the relationship between the contigent variables and the information characteristics of accounting information system. The research method adopted in this study is the survey. The results show that the environmental uncertainty, organization size, task diversity, task interrelatedness, and management commitment are positively related with the involvement and role of user in the system development process. The aggregate information regarding various departments tends to be produced by an organization under the uncertain environment. The information is reported periodically by a centralized organization. The aggregate information is preferred when tha task for implementation is diversified. The output information is more aggregate and provided more frequently when there exists a high interrelatioship between tasks. The output information is more external and reported more frequently in an organization with the high management commitment.

• Journal of Environmental Science International
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• v.13 no.7
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• pp.611-618
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• 2004

The Neural Network Models which mathematically interpret human thought processes were applied to resolve the uncertainty of model parameters and to increase the model's output for the streamflow forecast model. In order to test and verify the flood discharge forecast model eight flood events observed at Kumho station located on the midstream of Kumho river were chosen. Six events of them were used as test data and two events for verification. In order to make an analysis the Levengerg-Marquart method was used to estimate the best parameter for the Neural Network model. The structure of the model was composed of five types of models by varying the number of hidden layers and the number of nodes of hidden layers. Moreover, a logarithmic-sigmoid varying function was used in first and second hidden layers, and a linear function was used for the output. As a result of applying Neural Networks models for the five models, the N10-6model was considered suitable when there is one hidden layer, and the Nl0-9-5model when there are two hidden layers. In addition, when all the Neural Network models were reviewed, the Nl0-9-5model, which has two hidden layers, gave the most preferable results in an actual hydro-event.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1615-1625
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• 2013

Modelling and simulating the wind power intermittent behaviour are the basis of the planning and scheduling studies concerning wind power integration. The wind power outputs are evidently correlated in space and time and bring challenges in characterizing their behaviour. This paper provides a methodology to model and simulate the clustered wind power considering its spatio-temporal correlations using the theory of copula. The sampling approach captures the complex spatio-temporal connections among the wind farms by employing a conditional density function calculated using multidimensional copula function. The empirical study of real wind power measurement shows how the wind power outputs are correlated and how these correlations affect the overall uncertainty of clustered wind power output. The case study validates the simulation technique by comparing the simulated results with the real measurements.

• Smart Structures and Systems
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• v.4 no.5
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• pp.583-603
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• 2008

In this study, a real-time damage detection method using output-only acceleration signals and artificial neural networks (ANN) is developed to monitor the occurrence of damage and the location of damage in structures. A theoretical approach of an ANN algorithm that uses acceleration signals to detect changes in structural parameters in real-time is newly designed. Cross-covariance functions of two acceleration responses measured before and after damage at two different sensor locations are selected as the features representing the structural conditions. By means of the acceleration features, multiple neural networks are trained for a series of potential loading patterns and damage scenarios of the target structure for which its actual loading history and structural conditions are unknown. The feasibility of the proposed method is evaluated using a numerical beam model under the effect of model uncertainty due to the variability of impulse excitation patterns used for training neural networks. The practicality of the method is also evaluated from laboratory-model tests on free-free beams for which acceleration responses were measured for several damage cases.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.