• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.4
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• pp.551-561
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• 2001

In this paper, the large signal unified model is established for H4O GaAs pHEMT of GEC-Marconi using modified Curtice model. This unified model includes DC characteristic, small signal, and noise characteristic as various bias. Particularly, the model can simply and physically explain trans-conductance $(g_m)$ of pHEMT using modified Curtice model, and can tell the difference $g_m$, $R_ds$ at DC and these at AC through inclusion of internal RF-choke. The results of the established model built up using SDD in HP-Eessof show good agreement to the S/W measured data in DC, small signal, and noise characteristic. This model can also be applied to various computer aided analysis, such as linear simulation, 1-tone harmonic balance simulation, and multi-tone harmonic balance simulation, so the LNA(Low Noise Amplifier), oscillator, and mixer design has been shown using this model library.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.4 s.107
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• pp.351-357
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• 2006

In this paper, a direct $TM_{01}-TE_{11}$ mode converter of circular waveguide which is used the approximated design method of mode conversion between $TM_{01}$ and $TE_{11}$ modes is designed for radiation with the maximum directivity of high power energy into air (rom high power microwave antenna system. The proposed direct $TM_{01}-TE_{11}$ mode converter is calculated and designed with an approximated method for non-linear function about an extended non-constant serpentine type of circular waveguide. Then, the designed mode converter is optimized by length of 200 mm and efficiency above 95% by FIM simulation and achieved short length and high efficiency of the antenna by results of fabricated and measured characteristics.

• The korean journal of orthodontics
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• v.45 no.3
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• pp.105-112
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• 2015

Objective: A recently developed facial scanning method uses three-dimensional (3D) surface imaging with a light-emitting diode. Such scanning enables surface data to be captured in high-resolution color and at relatively fast speeds. The purpose of this study was to evaluate the accuracy and precision of 3D images obtained using the Morpheus 3D$^{(R)}$ scanner (Morpheus Co., Seoul, Korea). Methods: The sample comprised 30 subjects aged 24.34 years (mean $29.0{\pm}2.5$ years). To test the correlation between direct and 3D image measurements, 21 landmarks were labeled on the face of each subject. Sixteen direct measurements were obtained twice using digital calipers; the same measurements were then made on two sets of 3D facial images. The mean values of measurements obtained from both methods were compared. To investigate the precision, a comparison was made between two sets of measurements taken with each method. Results: When comparing the variables from both methods, five of the 16 possible anthropometric variables were found to be significantly different. However, in 12 of the 16 cases, the mean difference was under 1 mm. The average value of the differences for all variables was 0.75 mm. Precision was high in both methods, with error magnitudes under 0.5 mm. Conclusions: 3D scanning images have high levels of precision and fairly good congruence with traditional anthropometry methods, with mean differences of less than 1 mm. 3D surface imaging using the Morpheus 3D$^{(R)}$ scanner is therefore a clinically acceptable method of recording facial integumental data.

• Aerospace Engineering and Technology
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• v.5 no.2
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• pp.143-148
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• 2006

This article summarizes the results of sea transportation load measurements, which will be used as the reference to the sea transportation environment condition for the launch vehicle of KSLV-I. KSLV-I will be transported by Sea from Pusan to the NARO space center in Gohung, Chunnam province. Since the vibration load condition during sea transportation is considered as one of the important design parameters of KSLV-I and its transportation means, it is necessary to directly measure the environmental condition of sea transportation loads prior to establish the sea transportation plan in detail. This material includes the measured data of 3-axis linear accelerations and 3-axis angular rates on the board of a barge-ship, which is towed by a tug boat during shipping operation. This barge-ship is same class with one which will actually carry KSLV-I. The results show that the measured load condition during sea transportation is not severer than the reference data of MIL-STD-810F and Zenit-3SL launch system.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.852-859
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• 2009

Normally, to extract the defect in TFT-LCD inspection system, the image is obtained by using line scan camera or area scan camera which is achieved by CCD or CMOS sensor. Because of the limited dynamic range of CCD or CMOS sensor as well as the effect of the illumination, these images are frequently degraded and the important features are hard to decern by a human viewer. In order to overcome this problem, the feature vectors in the image are obtained by using the average intensity difference between defect and background based on the weber's law and the standard deviation of the background region. The defect detection method uses non-linear SVM (Supports Vector Machine) method using the extracted feature vectors. The experiment results show that the proposed method yields better performance of defect classification methods over conveniently method.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.11 no.1
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• pp.106-113
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• 1997

In this paper, we introduce the design method using CAE(Computer Aided Engineering) which is profitable in the compatibility and standardization of the developed product and in the reduction of construction time and price to develop and design a machine equipment. Particularly, we select the standard model to design ot develop from the large machinery to the super precision one, extract the peculiar characters of the model by the close analysis of the physical and technical part, can predict the previous result of experimental characteristics on objective dimensions through the analogical mathematical analysis, and can induce the design model demanded by user investigating optimal data in advance. We present the analogical algorithms and process method of design factors and restriction factors in the systematization design with computer. Then we analyze step functions for each systematization equipment and induce the process of technical data with actuator model.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Journal of Intelligence and Information Systems
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• v.14 no.3
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• pp.25-39
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• 2008

The objective of this study is to forecast the operational continuous ability using Artificial Neural Networks in battalion defensive operation for the commander decision making support. The forecasting of the combat result is one of the most complex issue in military science. However, it is difficult to formulate a mathematical model to evaluate the combat power of a battalion in defensive operation since there are so many parameters and high temporal and spatial variability among variables. So in this study, we used company combat power level data in Battalion Command in Battle Training as input data and used Feed-Forward Multilayer Perceptrons(MLP) and General Regression Neural Network (GRNN) to evaluate operational continuous ability. The results show 82.62%, 85.48% of forecasting ability in spite of non-linear interactions among variables. We think that GRNN is a suitable technique for real-time commander's decision making and evaluation of the commitment priority of troops in reserve.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.3
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• pp.73-85
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• 2002

The purpose of this study was to develop prediction models for plant species abundance by stream restoration. Generally the stream plant is affected by stream gemophology. So in this study, the relationship between the vegetation abundance and stream gemophology was developed by multiple regression analysis. The stream characteristics utilized in this study were longitudinal slope, transectional slope, micro-landforms through the longitudinal direction, riparian width and geometric mean diameter and biggest diameter of bed material, and cumulated coarse and fine sand weight portion. The Pyungchang River with mountainous watershed and the Kyungan stream and the Bokha stream in the agricultural region were selected and vegetation species abundance and stream characteristics were documented from the site at 2~3km intervals from the upper stream to the lower. The Models for predicting the vegetation abundance were developed by multiple regression analysis using SPSS statistics package. The linear relationship between the dependant(species abundance) and independant(stream characteristics) variables was tested by a graphical method. Longitudinal and transectional slope had a nonlinear relationship with species abundance. In the next step, the independance between the independant variables was tested and the correlation between independant and dependant variables was tested by the Pearson bivariate correlation test. The selected independant variables were transectional slope, riparian width, and cumulated fine sand weight portion. From the multiple regression analysis, the $R^2$for the Pyungchang river, Kyungan stream, Bokga stream were 0.651, 0.512 and 0.240 respectively. The natural stream configuration in the Pyungchang river had the best result and the lower $R^2$for Kyunan and Bokha stream were due to human impact which disturbed the natural ecosystem. The lowest $R^2$for the Bokha stream was due to the shifting sandy bed. If the stream bed is fugitive, the prediction model may not be valid. Using the multiple regression models, the vegetation abundance could be predicted with stream characteristics such as, transection slope, riaparian width, cumulated fine sand weigth portion, after stream restoration.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.