• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.243-260
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• 2018

Detection of damages in fibre reinforced plastic (FRP) composite structures is important from the safety and serviceability point of view. Usually, damage is realized as a local reduction of stiffness and if dynamic responses of the structure are sensitive enough to such changes in stiffness, then a well posed inverse problem can provide an efficient solution to the damage detection problem. Usually, such inverse problems are solved within the framework of pattern recognition. Support Vector Machine (SVM) Algorithm is one such methodology, which minimizes the weighted differences between the experimentally observed dynamic responses and those computed using the finite element model- by optimizing appropriately chosen parameters, such as stiffness. A damage detection strategy is hereby proposed using SVM which perform stepwise by first locating and then determining the severity of the damage. The SVM algorithm uses simulations of only a limited number of damage scenarios and trains the algorithm in such a way so as to detect damages at unknown locations by recognizing the pattern of changes in dynamic responses. A rectangular fiber reinforced plastic composite plate has been investigated both numerically and experimentally to observe the efficiency of the SVM algorithm for damage detection. Experimentally determined modal responses, such as natural frequencies and mode shapes are used as observable parameters. The results are encouraging since a high percentage of damage cases have been successfully determined using the proposed algorithm.

• Journal of Microbiology and Biotechnology
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• v.9 no.2
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• pp.230-233
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• 1999

A plasmid vector was constructed for the expression and secretion of Bacillus macerans cyclodextrin glucanotransferase (CGTase) in Bacillus subtilis. The vector, pUBACGT, was composed of the ribosome-binding sequence, signal sequence, and cgt gene from B. macerans under the control of amyR2, the promoter of amyE gene coding for $\alpha$-amylase from B. subtilis var. natto. Bacillus subtilis LKS88, a mutant strain lacking genes for an amylase and two proteases, was used as a host for the transformation of the plasmid vector. The transformants were selected on kanamycin-containing Luria-Bertani plates. The starch hydrolyzing activity was observed on the starch-containing plates by the iodine method and cyclodextrin-forming activity was detected in the culture medium. A SDS-PAGE analysis showed that most of the expressed CGTase in the recombinant B. subtilis was secreted into the medium at a high expression level.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.597-600
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• 2007

Face is considered to be one of the biometrics in person identification. But Face recognition is a high dimensional pattern recognition problem. Even low-resolution face images generate huge dimensional feature space. The aim of this paper is to present a fast feature extraction method for real time human face recognition. first, It compute eigen-vector and eigen-value by Principle component analysis on inputed human face image, and propose method of feature extraction that make feature vector by apply gabor filter to computed eigen-vector. And it compute feature value which multiply by made eigen-value. This study simulations performed using the ORL Database.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.421-428
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• 2002

The local prediction method utilizing embedding vector loses the prediction power when the parameter r estimation is not exact for predicting the chaos time series induced from the high order differential equation. In spite of the fact that there have been a lot of suggestions regarding how to estimate the delay time ($\tau$), no specific method is proposed to apply to any time series. The inclinded linear model, which utilizes inclinded netter, yields satisfying degree of prediction power without estimating exact delay time ($\tau$). The usefulness of this approach has been indicated not only theoretically but also in practical situation when the method w8s applied to economical time series analysis.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.8
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• pp.763-767
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• 2008

In this paper, to make a high performance and low power CORDIC architecture for 3D operations in mobile devices, we suggest two off-line vectoring algorithms named Angle Based Search (ABS) and Scaling Considered Search (SCS). The ABS algorithm represents a 3D vector with two angles and those angles are used as a condition for searching CORDIC rotation sequences. The SCS algorithm determines the best CORDIC rotation sequence in advance to eliminate extra scaling computation. Using the proposed algorithms, we can observe 50% of latency is reduced. Furthermore, we perform a simple analysis and discuss possible reduction of power consumption by applying voltage scaling method together with the proposed algorithm.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.4
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• pp.279-285
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• 2020

An open-end winding (OEW) permanent magnet synchronous motor with dual inverters can synthesize large voltages for a motor with the same DC link voltage. This ability has the advantage of reducing the use of DC/DC boost converters or high voltage batteries. However, zero-sequence voltage (ZSV), which is caused by the difference in the combined voltage between the primary and secondary inverters, can generate a zero-sequence current (ZSC) that increases system losses. Among the methods for eliminating this phenomenon, combining voltage vector eliminated ZSV cannot be accomplished by the conventional Pulse Width Modulation(PWM) method. In this study, a PWM carrier generation method using functionalization to generate a switching pattern to suppress ZSC is proposed and applied to analyze the control influence of the center-zero vector in the switching sequence about the current ripple.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.265-285
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• 2018

In this study, we applied a deep neural network model to predict four grades of fine dust $PM_{10}$, 'Good, Moderate, Bad, Very Bad' and two grades, 'Good or Moderate and Bad or Very Bad'. The deep neural network model and existing classification techniques (such as neural network model, multinomial logistic regression model, support vector machine, and random forest) were applied to fine dust daily data observed from 2010 to 2015 in six major metropolitan areas of Korea. Data analysis shows that the deep neural network model outperforms others in the sense of accuracy.

• Journal of IKEEE
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• v.4 no.2 s.7
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• pp.225-232
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• 2000

Dead time which is inserted in PWM signals of VSI distorts the inverter output voltage waveforms and deteriorate the control performance of an induction machine by producing torque ripples. In this paper, dead time compensation method in a vector controlled induction machine is proposed. The method is based on a feedforward approach that compensates dead time effect by adding the compensating voltages to the inverter output voltage references in 2 phase stationary frame. The proposed method is only software intensive and easy to realize without additional hardware. The experimental results show the validity and effectiveness of the proposed method.

• Journal of electromagnetic engineering and science
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• v.7 no.4
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• pp.161-168
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• 2007

A fast correlative vector direction finding(CVDF) system using active dipole antenna array for mobile direction finding(DF) applications is presented. To develop the CVDF system, the main elements such as active dipole antenna, multi-channel direction finder, and search receiver are designed and analyzed. The active antenna is designed as composite structure to improve the filed strength sensitivity over the wide frequency range, and the multi-channel direction finder and search receiver are designed using DDS-based PLL with settling time of below 35 us to achieve short signal processing time. This system provides the capabilities of the high DF sensitivity over the wide frequency range and allows for high probability of intercept and accurate angle of arrival(AOA) estimation for agile signals. The design and performance analysis according to the external noise and modulation schemes of the CVDF system with five-element circular array are presented in detail.

• Computers and Concrete
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• v.18 no.6
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• pp.1065-1082
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• 2016

The main aim of this study is to predict the compressive and flexural strengths of self-compacting mortar (SCM) containing $nano-SiO_2$, $nano-Fe_2O_3$ and nano-CuO using wavelet-based weighted least squares-support vector machines (WLS-SVM) approach which is called WWLS-SVM. The WWLS-SVM regression model is a relatively new metamodel has been successfully introduced as an excellent machine learning algorithm to engineering problems and has yielded encouraging results. In order to achieve the aim of this study, first, the WLS-SVM and WWLS-SVM models are developed based on a database. In the database, nine variables which consist of cement, sand, NS, NF, NC, superplasticizer dosage, slump flow diameter and V-funnel flow time are considered as the input parameters of the models. The compressive and flexural strengths of SCM are also chosen as the output parameters of the models. Finally, a statistical analysis is performed to demonstrate the generality performance of the models for predicting the compressive and flexural strengths. The numerical results show that both of these metamodels have good performance in the desirable accuracy and applicability. Furthermore, by adopting these predicting metamodels, the considerable cost and time-consuming laboratory tests can be eliminated.