• Journal of Korean Institute of Industrial Engineers
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• v.28 no.3
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• pp.331-343
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• 2002

This paper proposes a fuzzy rule-based model as a means to build usability models between emotional satisfaction and design variables of consumer products. Based on a subtractive clustering algorithm, this model obtains partially overlapping rules from existing data and builds multiple local models each of which has a form of a linear regression equation. The best subset procedure and cross validation technique are used to select appropriate input variables. The proposed technique was applied to the modeling of luxuriousness, balance, and attractiveness of office chairs. For comparison, regression models were built on the same data in two different ways; one using only potentially important variables selected by the design experts, and the other using all the design variables available. The results showed that the fuzzy rule-based model had a great benefit in terms of the number of variables included in the model. They also turned out to be adequate for predicting the usability of a new product. Better yet, the information on the product classes and their satisfaction levels can be obtained by interpreting the rules. The models, when combined with the information from the regression models, are expected to help the designers gain valuable insights in designing a new product.

• Journal of Electrical Engineering and Technology
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• v.2 no.2
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• pp.231-240
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• 2007

This paper presents an advanced modeling and simulation technique applied to DC/DC power electronic converters fed through renewable energy power sources. The distributed generation (DG) system at the Illinois Institute of Technology, which employs a phase-l system consisting of a photovoltaic-based power system and a phase-2 system consisting of a fuel cell based primary power source, is studied. The modeling and simulation of the DG system is done using the generalized state space averaging (GSSA) method. Furthermore, the paper compares the results achieved upon simulation of the specific GSSA models with those of popular computer aided design software simulations performed on the same system. Finally, the GSSA and CAD software simulation results are accompanied with test results achieved via experimentation on both, the PV-based phase-l system and the fuel cell based phase-2 power system.

• Tunnel and Underground Space
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• v.15 no.2 s.55
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• pp.129-136
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• 2005

This paper describes the implementation of rock mass rating evaluation based on genetic algorithm(GA) and conditional simulation technique to estimate RMR in the area without sufficient borehole data RMR were estimated by GA and conditional simulation technique with reflecting distribution feature and spatial correlation. And RMR determined by GA were compared with the results from kriging. Through the analysis of the results from 30 simulations, the uncertainty of estimation could be quantified.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.367-372
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• 2006

Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.

• Smart Structures and Systems
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• v.15 no.1
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• pp.41-55
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• 2015

This study proposes a laser based impedance measurement system and impedance based pipe corrosion and bolt-loosening monitoring techniques under temperature variations. For impedance measurement, the laser based impedance measurement system is optimized and adopted in this paper. First, a modulated laser beam is radiated to a photodiode, converting the laser beam into an electric signal. Then, the electric signal is applied to a MFC transducer attached on a target structure for ultrasonic excitation. The corresponding impedance signals are measured, re-converted into a laser beam, and radiated back to the other photodiode located in a data interrogator. The transmitted impedance signals are treated with an outlier analysis using generalized extreme value (GEV) statistics to reliably signal off structural damage. Validation of the proposed technique is carried out to detect corrosion and bolt-loosening in lab-scale carbon steel elbow pipes under varying temperatures. It has been demonstrated that the proposed technique has a potential to be used for structural health monitoring (SHM) of pipe structures.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.17-22
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• 2021

Radar signal analysis of electronic warfare is a technique for identifying a radar type by signal parameters(direction, radion frequency, pulse repetition interval, pulse width, scan period..) extracted from a received radar pulse. However as the modern radar and new threat environments is advanced, radar identification ambiguity arises in the process of identifying the types of radars. In this paper, we analyze the problems of the existing method and propose a new method. This technique determines the validity of the scan period by the difference in the arrival time of the radar pulse and the minimum number of scan period discrimination. Experiments proved that the scan cycle results are derived regardless of the RMS((Root Mean Square) of the input amplitude.

• Journal of the Korean Ceramic Society
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• v.56 no.1
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• pp.37-48
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• 2019

The objective of this research is to analyze the fracture toughness of pure and silica co-doped yttria-stabilized tetragonal zirconia polycrystal (3Y-TZP) bioceramics containing 0.1 and 0.2 wt.% of alumina, and sintered at a temperature of $1500^{\circ}C$. Because of the relatively easy preparation of the test specimens and the high speed of testing, the Vickers indentation fracture (VIF) technique is more frequently used to evaluate the fracture toughness of biomaterials and hard biological tissues. The Young's modulus and hardness values were obtained by means of nanoindentation and indentation methods. The fracture toughness values of 3Y-TZP bioceramics were calculated and analyzed using 15 equations related to the VIF technique, and loadings of 49.03 and 196.13 N with a Vickers diamond. For validation, the results were compared with fracture toughness values obtained by the single-edge laser-notch beam (SELNB) method with an almost atomically sharp laser-machined initial notch.

• Corrosion Science and Technology
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• v.20 no.2
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• pp.52-61
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• 2021

This study using experimental design and linear regression technique was implemented in order to predict the pitting potential of stainless steel in marine environments, with the target materials being AL-6XN and STS 316L. The various variables (inputs) which affect stainless steel's pitting potential included the pitting resistance equivalent number (PRNE), temperature, pH, Cl^- concentration, sulfate levels, and nitrate levels. Among them, significant factors affecting pitting potential were chosen through an experimental design method (screening design, full factor design, analysis of variance). The potentiodynamic polarization test was performed based on the experimental design, including significant factor levels. From these testing methods, a total 32 polarization curves were obtained, which were used as training data for the linear regression model. As a result of the model's validation, it showed an acceptable prediction performance, which was statistically significant within the 95% confidence level. The linear regression model based on the full factorial design and ANOVA also showed a high confidence level in the prediction of pitting potential. This study confirmed the possibility to predict the pitting potential of stainless steel according to various variables used with experimental linear regression design.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1097-1107
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• 2020

Assessment of soil carbon stock is essential for climate change mitigation and soil fertility. The digital soil mapping (DSM) is well known as a general technique to estimate the soil carbon stocks and upgrade previous soil maps. The aim of this study is to calculate the soil carbon stock in the top soil layer (0 to 30 cm) in Jeolla Province of South Korea using the DSM technique. To predict spatial carbon stock, we used Cubist, which a data-mining algorithm model base on tree regression. Soil samples (130 in total) were collected from three depths (0 to 10 cm, 10 to 20 cm, 20 to 30 cm) considering spatial distribution in Jeolla Province. These data were randomly divided into two sets for model calibration (70%) and validation (30%). The results showed that clay content, topographic wetness index (TWI), and digital elevation model (DEM) were the most important environmental covariate predictors of soil carbon stock. The predicted average soil carbon density was 3.88 kg·m-2. The R2 value representing the model's performance was 0.6, which was relatively high compared to a previous study. The total soil carbon stocks at a depth of 0 to 30 cm in Jeolla Province were estimated to be about 81 megatons.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2297-2310
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• 2022

A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.