• Journal of Electrical Engineering and Technology
• /
• v.10 no.1
• /
• pp.308-313
• /
• 2015

This paper describes a technique for complete identification of a two-dimensional scattering object and multiple objects immersed in air using microwaves where the scatterers are assumed to be a homogenous dielectric medium. The employed technique consists of initially retrieving the shape and position of the scattering object using a linear sampling method and then determining the electric permittivity and conductivity of the scatterer using adjoint sensitivity analysis. Incident waves are assumed to be TM (Transverse Magnetic) plane waves. This inversion algorithm results in high computational speed and efficiency, and it can be generalized for any scatterer structure. Also, this method is robust with respect to noise. The numerical results clearly show that this hybrid approach provides accurate reconstructions of various objects.

• Journal of Navigation and Port Research
• /
• v.32 no.1
• /
• pp.81-88
• /
• 2008

The forecast of a container traffic has been very important for port plan and development. Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate that effectiveness can differ according to the characteristics of ports.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2007.12a
• /
• pp.259-260
• /
• 2007

The forecast of a container traffic has been very important for port plan and development Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest tint ANNs am be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate tint effectiveness can differ according to the ch1racteristics of ports.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.6
• /
• pp.1571-1578
• /
• 2013

With the emergence of high-integration array and large area panel process, the need to minimize the generation of particles in the field of semiconductor, LCD and OLED has grown. As an alternative to the conventional roller system, a contactless magnetic conveyor has been proposed to reduce the generation of particles. An EM-PM hybrid which is one of magnetic levitation types is already proposed for the conveyor system. One of problems pointed out with this approach is the vibration caused by the dynamic interaction between conveyor and rail. To reduce the vibration, the introduction of a secondary suspension system which aims to decouple the levitation electromagnet from the main body is proposed. The objective of this study is to develop a dynamic model for the magnetically levitated conveyor, and to investigate the effect of the introduced suspension system. An integrated model of levitation system and rail based on 3D multibody dynamic model is proposed. With the proposed model, the dynamic characteristics of maglev conveyor system are analyzed, and the effect of the secondary suspension and the stiffness and damping are investigated.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06a
• /
• pp.400-402
• /
• 2006

Matrix multiplication is an important problem in linear algebra. its main significance for combinatorial algorithms is its equivalence to a variety of other problems, such as transitive closure and reduction, solving linear systems, and matrix inversion. Thus the development of high-performance matrix multiplication implies faster algorithms for all of these problems. In this paper. we present a quantitative comparison of the theoretical and empirical performance of key matrix multiplication algorithms and use our analysis to develop a faster algorithm. We propose a Hybrid approach on Winograd's and Strassen's algorithms that improves the performance and discuss the performance of the hybrid Winograd-Strassen algorithm. Since Strassen's algorithm is based on a $2{\times}2$ matrix multiplication it makes the implementation very slow for larger matrix because of its recursive nature. Though we cannot get the theoretical threshold value of Strassen's algorithm, so we determine the threshold to optimize the use of Strassen's algorithm in nodes through various experiments and provided a summary shown in a table and graphs.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.22 no.3
• /
• pp.75-87
• /
• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Computational Structural Engineering : An International Journal
• /
• v.3 no.1
• /
• pp.19-29
• /
• 2003

In the present study, finite element linear buckling analysis is performed for grid-stiffened composite plates. A hybrid element with drilling degrees of freedom is employed to reduce the effect of the sensitivity of mesh distortion and to match the degrees of freedom between skins and stiffeners. The preliminary static stress distribution is analyzed for the determination of accurate load distribution. Parametric study of grid structures is performed and three types of buckling modes are observed. The maximum limit of buckling load was found at the local skin-buckling mode. In order to maximize buckling loads, stiffened panels need to be designed to be buckled in skin-buckling mode.

• Land and Housing Review
• /
• v.4 no.2
• /
• pp.185-192
• /
• 2013

This study presents application effects of hybrid seismic isolation system to realize high seismic performance for low-rise lightweight buildings through a non-linear analysis and onsite experiments. The complex seismic isolation system applied in this study is a method of mixing sliding bearing and laminated rubber bearing in order to overcome limitation of laminated rubber bearing in increasing natural period of the whole seismic isolation system. As a result of the non-linear analysis, seismic isolation buildings designed with complex seismic isolation system are safe because its maximum response displacement is within allowable design displacement even for a strong earthquake which rarely occurs and its maximum response shear is less than design seismic force. As a result of the onsite experiment, the rigidity of seismic isolation stories corresponds to approximately 95.8% of the design equivalent stiffness value. This indicates that actual properties of the whole seismic isolation system correspond to design values.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.21-31
• /
• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• Journal of Electrical Engineering and Technology
• /
• v.6 no.1
• /
• pp.94-103
• /
• 2011

This paper presents a novel bearingless switched reluctance motor (BLSRM) with decoupled torque and suspending stator poles. BLSRM is different from conventional bearingless switched reluctance motors (SRMs) because its suspending poles are separated from the torque poles. Perpendicularly placed suspending poles are designed to produce a continuous radial force to suspend the rotor. Due to the independent suspending and torque poles, BLSRM produces a suspending force with excellent linearity according to the rotor position and independent characteristics of the torque current. The air-gap is easier to control than in conventional SRMs with their linear and independent characteristics. Furthermore, to verify the proposed structure, a mathematical model for the suspending force is derived. Finite element analysis is also employed to compare BLSRM and conventional SRMs expressions of suspending force. A prototype motoris designed and manufactured to verify the effectiveness of the proposed bearingless structure.