• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.595-602
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• 2009

Fuzzy rules, which represent the behavior of their system, are sensitive to fuzzy clustering techniques. If the classification abilities of such clustering techniques are improved, their systems can work for the purpose more accurately because the capabilities of the fuzzy rules and parameters are enhanced by the clustering techniques. Thus, this paper proposes a new hierarchically structured clustering algorithm that can enhance the classification abilities. The proposed clustering technique consists of two clusters based on correlationship and statistical characteristics between data, which can perform classification more accurately. In addition, this paper uses difference data sets to reflect the patterns and regularities of the original data clearly, and constructs multiple fuzzy systems to consider various characteristics of the differences suitably. To verify effectiveness of the proposed techniques, this paper applies the constructed fuzzy systems to the field of time series prediction, and performs prediction for nonlinear time series examples.

• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.105-113
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• 2017

This study considers an optimal investment planning for improving survivability from an air threat in the layered air defense system. To establish an optimization model, we first represent the layered air defense system as a network model, and then, present two optimization models minimizing the failure probability of counteracting an air threat subject to budget limitation, in which one deals with whether to invest and the other enables continuous investment on the subset of nodes. Nonlinear objective functions are linearized using log function, and we suggest dynamic programming algorithm and linear programing for solving the proposed models. After designing a layered air defense system based on a virtual scenario, we solve the two optimization problems and analyze the corresponding optimal solutions. This provides necessity and an approach for an effective investment planning of the layered air defense system.

• Nuclear Engineering and Technology
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• v.22 no.1
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• pp.1-11
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• 1990

Heavy water is used as a moderator and a coolant in the pressurized heavy water reactor Because of the high cost of heavy water, downgraded heavy water generated in the reactor system is recycled to the reactor after being concentrated up to 99.8% or more in heavy water upgraders. This study investigates the process of upgraders and then suggests a theoretical model. The relations between process variables are derived from tower packing characteristics, vapour-liquid equilibria, and mass-heat balance equations at a steady state operation of the upgrader h computer program UPGR is developed, using the algorithm that solves the nonlinear equations step by step. It shows that the results of computer simulation are in good agreement with the operating data of the Wolsung upgrader. Thus, this computer code offers the optimum operating guide and is now applied to manage the performance of upgraders for the effective operation of the heavy water upgraders.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1131-1138
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• 2019

The importance of coatings has been increasing for different purposes such as prevention of static electricity of auto parts or products, improvement of abrasion and corrosion resistance, and enhancement of esthetics. As a method for measuring the thickness of a coating film, a contact method with probe is commonly used. However, it is problematic that accuracy of the sensor is degraded due to sensor output distortion or load phenomenon, which is caused by a change in magnetic permeability of the core. In this study, we propose a method to reduce the measurement error of the coating film by applying the optimized circuit design and the thickness measurement algorithm to the problems caused by the nonlinear characteristics. The tests result which have been taken with different thickness coating samples show that the measurement accuracy is within ${\pm}2%$.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.1
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• pp.139-146
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• 2003

In this paper we demonstrate a cryptanalysis of the stream cipher LILI-128. Our approach to analysis on LILI-128 is to solve an overdefined system of multivariate equations. The LILI-128 keystream generato $r^{[8]}$ is a LFSR-based synchronous stream cipher with 128 bit key. This cipher consists of two parts, “CLOCK CONTROL”, pan and “DATA GENERATION”, part. We focus on the “DATA GENERATION”part. This part uses the function $f_d$. that satisfies the third order of correlation immunity, high nonlinearity and balancedness. But, this function does not have highly nonlinear order(i.e. high degree in its algebraic normal form). We use this property of the function $f_d$. We reduced the problem of recovering the secret key of LILI-128 to the problem of solving a largely overdefined system of multivariate equations of degree K=6. In our best version of the XL-based cryptanalysis we have the parameter D=7. Our fastest cryptanalysis of LILI-128 requires $2^{110.7}$ CPU clocks. This complexity can be achieved using only $2^{26.3}$ keystream bits.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.706-713
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• 2020

To control the LED lighting for broadcasting, LED current control using tri-stimulus values is used for RGB LEDs. For the convenience of control, this control is approximated as a linear function or used as an appropriate value through trial and error. Also, it is not suitable for broadcast lighting because it does not use a diffuser plate applied for mixing sufficient light and color required for actual it. In this study, a neural network with excellent nonlinear function approximation is used as a control method for LED panels for broadcast lighting. We intend to implement an LED panels controller suitable for the desired chromaticity coordinates and dimming values of intensity. As a result of the performance evaluation, the errors of the xy chromaticity coordinates are mostly ±0.02 and the acceptable range of ANSI C78.377A was satisfied. The average errors of the xy chromaticity coordinate are xerror=0.0044 and yerror=0.0030, respectively, and we confirmed the superiority and stable performance of the proposed algorithm.

• Journal of Radiation Protection and Research
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• v.45 no.4
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• pp.171-177
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• 2020

Background: This study aims to determine the effective atomic number (Zeff) from dual-energy image sets obtained using a conventional computed tomography (CT) simulator. The estimated Zeff can be used for deriving the stopping power and material decomposition of CT images, thereby improving dose calculations in radiation therapy. Materials and Methods: An electron-density phantom was scanned using Philips Brilliance CT Big Bore at 80 and 140 kVp. The estimated Zeff values were compared with those obtained using the calibration phantom by applying the Rutherford, Schneider, and Joshi methods. The fitting parameters were optimized using the nonlinear least squares regression algorithm. The fitting curve and mass attenuation data were obtained from the National Institute of Standards and Technology. The fitting parameters obtained from stopping power and material decomposition of CT images, were validated by estimating the residual errors between the reference and calculated Zeff values. Next, the calculation accuracy of Zeff was evaluated by comparing the calculated values with the reference Zeff values of insert plugs. The exposure levels of patients under additional CT scanning at 80, 120, and 140 kVp were evaluated by measuring the weighted CT dose index (CTDIw). Results and Discussion: The residual errors of the fitting parameters were lower than 2%. The best and worst Zeff values were obtained using the Schneider and Joshi methods, respectively. The maximum differences between the reference and calculated values were 11.3% (for lung during inhalation), 4.7% (for adipose tissue), and 9.8% (for lung during inhalation) when applying the Rutherford, Schneider, and Joshi methods, respectively. Under dual-energy scanning (80 and 140 kVp), the patient exposure level was approximately twice that in general single-energy scanning (120 kVp). Conclusion: Zeff was calculated from two image sets scanned by conventional single-energy CT simulator. The results obtained using three different methods were compared. The Zeff calculation based on single-energy exhibited appropriate feasibility.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.103-108
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• 2022

In this paper, we propose a new image analysis method based on regression manifold 3-D PCA. The proposed method is a new image analysis method consisting of a regression analysis algorithm with a structure designed based on an autoencoder capable of nonlinear expansion of manifold 3-D PCA and PCA for efficient dimension reduction when entering large-capacity image data. With the configuration of an autoencoder, a regression manifold 3-DPCA, which derives the best hyperplane through three-dimensional rotation of image pixel values, and a Bayesian rule structure similar to a deep learning structure, are applied. Experiments are performed to verify performance. The image is improved by utilizing the fine dust image, and accuracy performance evaluation is performed through the classification model. As a result, it can be confirmed that it is effective for deep learning performance.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.