• International Journal of Fluid Machinery and Systems
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• v.10 no.1
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• pp.63-75
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• 2017

Many axial fans have circular arc blades with constant thickness. It is still a challenging task to calculate their performance, i.e. to predict how large their pressure rise and pressure losses are. For this task a need for cascade data exists. Therefore, the designer needs a method which works quickly for design purposes. In the present contribution a design method for such cascades consisting of circular arc blades with constant thickness is described. It is based on a singularity method which is combined with a CFD-data-based flow loss model. The flow loss model uses CFD-data to predict the total pressure losses. An interpolation method for the CFD-data are applied and described in detail. Data of measurements are used to validate the CFD-data and parameter variations are conducted. The parameter variations include the variation of the camber angle, pitch chord ratio and the Reynolds number. Additionally, flow patterns of two dimensional cascades consisting of circular arc blades with constant thickness are shown.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.185-207
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• 1998

This research investigates computer generated hybrid second-order model of two numerically based approaches to risk classification : discriminant analysis and neural networks. The hybrid second-order models are derived by rule induction using the ID3 and tested in the several different kinds of data. This new hybrid approach is designed to combine the high prediction accuracy and robustness of DA or NN with perspicuity of ID3. The hybrid model also eliminates the problem of contradictory inputs of ID3. After doing empirical test for the validity of hybrid model using small and medium companies' bankrupt data, hybrid model shows high perspicuity, high prediction accuracy for bankrupt, and simplicity for rules. The hybrid model also shows high performance regardless the type of data such as numeric data, non-numeric data, and combined data.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.538-549
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• 2019

Cloud computing is the concept of providing information technology services on the Internet, such as software, hardware, networking, and storage. These services can be accessed anywhere at any time on a pay-per-use basis. However, storing data on servers is a challenging aspect of cloud computing. This paper utilizes cryptography and access control to ensure the confidentiality, integrity, and proper control of access to sensitive data. We propose a model that can protect data in cloud computing. Our model is designed by using an enhanced RSA encryption algorithm and a combination of role-based access control model with extensible access control markup language (XACML) to facilitate security and allow data access. This paper proposes a model that uses cryptography concepts to store data in cloud computing and allows data access through the access control model with minimum time and cost for encryption and decryption.

• Journal of Environmental Science International
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• v.8 no.3
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• pp.355-361
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• 1999

The aim of this study is to develop the water quality simulation model (BAYQUAL) that deal with the physical, chemical and biological aspects of fate/behavior of pollutants in the bay. BAYQUAL is a two dimensional, time-variable finite element water quality model based on the flow simulation model in bay(BAYFLOW). The algorithm is composed of a hydrodynamic module which solves the equations of motion and continuity, a pollutnat dispersion module which solves the dispersion-advection equation. The applicability and feasibility of the model are discussed by applications of the model to the Kwangyang bay of south coastal waters of Korea. Based on the field data, the BAYQUAL model was calibrated and verified. The results were in good agreement with measured value within relative error of 14% for COD, T-N, T-P. Numerical simulations of velocity components and tide amplitude(M2) were agreed closely with the actual data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.41-43
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• 2003

A radar scattering model is developed based on an empirical rough surface scattering model, the radiative transfer model (RTM), a numerical simulation algorithm of radar scattering from particles, and experimental data obtained by ground-based scatterometers and SAR systems. At first, the scattering matrices of scattering particles such as a leaf, a branch, and a trunk, have been modeled using the physical optics (PO) model and the numerical full-wave analysis. Then, radar scattering from a group of mixed particles has been modeled using the RTM, which leads to a general scattering model for earth surfaces. Finally, the scattering model has been verified with the experimental data obtained by scatterometers and SAR systems.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.503-509
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• 2020

This study aims to develop a patient-specific radiation exposure dose prediction model based on anthropometric data that can be easily measurable during CT examination, and to be used as basic data for DRL setting and radiation dose management system in the future. In addition, among the machine learning algorithms, the most suitable model for predicting exposure doses is presented. The data used in this study were chest CT scan data, and a data set was constructed based on the data including the patient's anthropometric data. In the pre-processing and sample selection of the data, out of the total number of samples of 250 samples, only chest CT scans were performed without using a contrast agent, and 110 samples including height and weight variables were extracted. Of the 110 samples extracted, 66% was used as a training set, and the remaining 44% were used as a test set for verification. The exposure dose was predicted through random forest, linear regression analysis, and SVM algorithm using Orange version 3.26.0, an open software as a machine learning algorithm. Results Algorithm model prediction accuracy was R^2 0.840 for random forest, R^2 0.969 for linear regression analysis, and R^2 0.189 for SVM. As a result of verifying the prediction rate of the algorithm model, the random forest is the highest with R^2 0.986 of the random forest, R^2 0.973 of the linear regression analysis, and R^2 of 0.204 of the SVM, indicating that the model has the best predictive power.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.132-132
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• 2015

Virtual metrology (VM) model based on plasma information (PI) parameter for C4F8 plasma-assisted oxide etching processes is developed to predict and monitor the process results such as an etching rate with improved performance. To apply fault detection and classification (FDC) or advanced process control (APC) models on to the real mass production lines efficiently, high performance VM model is certainly required and principal component regression (PCR) is preferred technique for VM modeling despite this method requires many number of data set to obtain statistically guaranteed accuracy. In this study, as an effective method to include the 'good information' representing parameter into the VM model, PI parameters are introduced and applied for the etch rate prediction. By the adoption of PI parameters of b-, q-factors and surface passivation parameters as PCs into the PCR based VM model, information about the reactions in the plasma volume, surface, and sheath regions can be efficiently included into the VM model; thus, the performance of VM is secured even for insufficient data set provided cases. For mass production data of 350 wafers, developed PI based VM (PI-VM) model was satisfied required prediction accuracy of industry in C4F8 plasma-assisted oxide etching process.

• International Journal of Control, Automation, and Systems
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• v.2 no.2
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• pp.256-262
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• 2004

Conventionally, profile non-uniformity has been characterized by relying on approximated profile with angle or anisotropy. In this study, a new non-uniformity model for etch profile is presented by applying a discrete wavelet to the image obtained from a scanning electron microscopy (SEM). Prediction models for wavelet-transformed data are then constructed using a back-propagation neural network. The proposed method was applied to the data collected from the etching of tungsten material. Additionally, 7 experiments were conducted to obtain test data. Model performance was evaluated in terms of the average prediction accuracy (APA) and the best prediction accuracy (BPA). To take into account randomness in initial weights, two hundred models were generated for a given set of training factors. Behaviors of the APA and BPA were investigated as a function of training factors, including training tolerance, hidden neuron, initial weight distribution, and two slopes for bipolar sig-moid and linear function. For all variations in training factors, the APA was not consistent with the BPA. The prediction accuracy was optimized using three approaches, the best model based approach, the average model based approach and the combined model based approach. Despite the largest APA of the first approach, its BPA was smallest compared to the other two approaches.

• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.115-121
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• 2012

This paper, being the second in a two-part series, presents the robust performance of the proposed design method which can enhance a reliability-based design optimization(RBDO) under the uncertainties of probabilistic models. The robust performances of the solutions obtained by the proposed method, described in the Part 1, are investigated through the parametric studies. A 10-bar truss example is considered, and the uncertain parameters include the number of data observed, and the variations of applied loadings and allowable stresses. The numerical results show that the proposed method can produce a consistent result despite of the large variations in the parameters. Especially, even with the relatively small data set, the analysis results show that the exact probabilistic model can be successfully predicted with optimized design sections. This consistency of estimating appropriate probability model is also observed in the case of the variations of other parameters, which verifies the robustness of the proposed method.

• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.189-208
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• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.