• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.264-270
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• 2011

A new method for creating a logical data model is proposed. The logical data model developed by the method defines table, primary key, foreign key, and fields. The framework of the logical data model is constructed by modeling the relationships between events and their related entity types. The proposed method consists of a series of objective and quantitative decisions such as maximum cardinality of relationships and functional dependency between the primary key and attributes. Even beginners to database design can use the methology as long as they understand such basic concepts about relational databases as primary key, foreign key, relationship cardinality, parent-child relationship, and functional dependency. The simple and systematic approach minimizes decision errors made by a database designer. In practial database design the method creates a logical data model in Boyce-Codd normal form unless the user of the method makes a critical decision error, which is very unlikely.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.2
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• pp.225-234
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• 2018

A Multibeam Echo Sounder (MBES) is commonly used for rapid seafloor mapping. We herein present a time-domain integrated system simulation technique for MBES development. The Modeling and Simulation (M&S) modules consist of four parts: sensor array signal transmission, propagation and backscattering modeling in the ocean environment, beamforming of the received signals, and image processing. Also, the simulation employs a ray-theory-based algorithm to correct the reconstructed bathymetry, which has errors due to the refraction caused by the vertical sound velocity profile. The developed M&S technique enables design parameter verification and system parameter optimization for MBES. The framework of this technique can also be potentially used to characterize the seabed properties. Finally, typical seafloor images are presented and discussed.

• ETRI Journal
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• v.45 no.4
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• pp.557-569
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• 2023

Traditional deterministic channel modeling is accurate in prediction, but due to its complexity, improving computational efficiency remains a challenge. In an alternative approach, we investigated a multilayer artificial neural network (ANN) to predict large-scale and small-scale channel characteristics in metro tunnels. Simulated high-precision training datasets were obtained by combining measurement campaign with a ray tracing (RT) method in a metro tunnel. Performance on the training data was used to determine the number of hidden layers and neurons of the multilayer ANN. The proposed multilayer ANN performed efficiently (10 s for training; 0.19 ms for prediction), and accurately, with better approximation of the RT data than the single-layer ANN. The root mean square errors (RMSE) of path loss (2.82 dB), root mean square delay spread (0.61 ns), azimuth angle spread (3.06°), and elevation angle spread (1.22°) were impressive. These results demonstrate the superior computing efficiency and model complexity of ANNs.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.475-480
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• 2023

When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.

• Advances in Computational Design
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• v.9 no.2
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• pp.81-96
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• 2024

Modeling and analyzing the dynamic behavior of fluid-soil-structure interaction problems are crucial in structural engineering. The solution to such coupled engineering systems is often not achievable through analytical modeling alone, and a numerical solution is necessary. Generally, the Finite Element Method (FEM) is commonly used to address such problems. However, when dealing with coupled problems with complex geometry, the finite element method may not precisely represent the geometry, leading to errors that impact solution quality. Recently, Isogeometric Analysis (IGA) has emerged as a preferred method for modeling and analyzing complex systems. In this study, IGA based on Non-Uniform Rational B-Splines (NURBS) is employed to analyze the seismic behavior of concrete gravity dams, considering fluid-structure-foundation interaction. The performance of IGA is then compared with the classical finite element solution. The computational efficiency of IGA is demonstrated through case studies involving simulations of the reservoir-foundation-dam system under seismic loading.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.577-582
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• 2002

Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.

• Smart Structures and Systems
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• v.15 no.6
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• pp.1439-1461
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• 2015

Design criteria, modeling rules, and analysis principles of seismic isolation systems have already found place in important building codes and standards such as the Uniform Building Code and ASCE/SEI 7-05. Although real behaviors of isolation systems composed of high damping or lead rubber bearings are nonlinear, equivalent linear models can be obtained using effective stiffness and damping which makes use of linear seismic analysis methods for seismic-isolated buildings possible. However, equivalent linear modeling and analysis may lead to errors in seismic response terms of multi-story buildings and thus need to be assessed comprehensively. This study investigates the accuracy of equivalent linear modeling via numerical experiments conducted on generic five-story three dimensional seismic-isolated buildings. A wide range of nonlinear isolation systems with different characteristics and their equivalent linear counterparts are subjected to historical earthquakes and isolation system displacements, top floor accelerations, story drifts, base shears, and torsional base moments are compared. Relations between the accuracy of the estimates of peak structural responses from equivalent linear models and typical characteristics of nonlinear isolation systems including effective period, rigid-body mode period, effective viscous damping ratio, and post-yield to pre-yield stiffness ratio are established. Influence of biaxial interaction and plan eccentricity are also examined.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.532-541
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• 2018

Decision support systems are a key focus of research on developing control rooms to aid operators in making reliable decisions and reducing incidents caused by human errors. For this purpose, models of complex systems can be developed to diagnose causes or consequences for specific alarms. Models applied in safety systems of complex and safety-critical systems require rigorous and reliable model building and testing. Multilevel flow modeling is a qualitative and discrete method for diagnosing faults and has previously only been validated by subjective and qualitative means. To ensure reliability during operation, this work aims to synthesize a procedure to measure model performance according to diagnostic requirements. A simple procedure is proposed for validating and evaluating the concept of multilevel flow modeling. For this purpose, expert statements, dynamic process simulations, and pilot plant experiments are used for validation of simple multilevel flow modeling models of a hydrocyclone unit for oil removal from produced water.

• Journal of the Korea Computer Industry Society
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• v.3 no.7
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• pp.837-844
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• 2002

In general, software test is carried out to detect or repair errors in system during software development process. Namely, we can evaluate software reliability through collecting and removing the faults detected in testing phase. Software reliability growth model evaluates reliability of software mathematically. Many kinds of software reliability growth modeling which modeling the processes of detecting, revising and removing the faults detected in testing phase have been proposed in many ways. and, it is assumed that almost of these modeling have one typed detect and show the uniformed detection rate. In this study, significance levels of the faults detected in test phase are classified according to how they can affect on the whole system and then the fault detection capability of them is applied. From this point of view, We here by propose a software reliability growth model with faults detection capability according considering fault significance levels and apply some fault data to this proposed model and finally verify its validity by comparing and estimating with the existing modeling.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.2
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• pp.1-9
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• 2014

A modeling system is constructed by integrating an one-dimensional unsteady flow simulation model and a hydrologic model to simulate flood flows in drainage channel networks of paddy field districts. The modeling system's applicability is validated by simulating flood discharges from a paddy field district, which consists of nine paddy fields and one drainage channel. The simulation results are in good agreement with the observed. Particularly, in the verification stage, the relative errors of peak flows and peak depths between the observed and simulated hydrographs range 8.96 to 10.26 % and -10.26 to 2.97 %, respectively. The modeling system's capability is compared with that of a water balance equation-based model; it is revealed that the modeling system's accuracy is superior to the other model. In addition, the simulations of flood discharges from large-sized paddy fields through drainage channels show that the flood discharge patterns are affected by drainage outlet management for paddy fields and physical characteristics of the drainage channels. Finally, it is concluded that to efficiently design drainage channel networks, it is necessary to analyze the results from simulating flood discharges of the drainage channel networks according to their physical characteristics and connectivities.