• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.133-138
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• 2003

The prediction of protein secondary structure has been an important bioinformatics tool that is an essential component of the template-based protein tertiary structure prediction process. It has been known that the predicted secondary structure information improves both the fold recognition performance and the alignment accuracy. In this paper, we describe several novel ideas that may improve the prediction accuracy. The main idea is motivated by an observation that the protein's structural information, especially when it is combined with the evolutionary information, significantly improves the accuracy of the predicted tertiary structure. From the non-redundant set of protein structures, we derive the 'potential' parameters for the protein secondary structure prediction that contains the structural information of proteins, by following the procedure similar to the way to derive the directional information table of GOR method. Those potential parameters are combined with the frequency matrices obtained by running PSI-BLAST to construct the feature vectors that are used to train the support vector machines (SVM) to build the secondary structure classifiers. Moreover, the problem of huge model file size, which is one of the known shortcomings of SVM, is partially overcome by reducing the size of training data by filtering out the redundancy not only at the protein level but also at the feature vector level. A preliminary result measured by the average three-state prediction accuracy is encouraging.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1812-1821
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• 2014

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.

• Steel and Composite Structures
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• v.14 no.4
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• pp.367-377
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• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

• Advances in aircraft and spacecraft science
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• v.7 no.4
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• pp.353-369
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• 2020

This paper presents the flutter analysis and optimum design of axially functionally graded box beam cantilever wing section by considering various geometric and material parameters. The coupled dynamic equations of the continuous model of wing system in terms of material and cross-sectional properties are formulated based on extended Hamilton's principle. By expressing the lift and pitching moment in terms of plunge and pitch displacements, the resultant two continuous equations are simplified using Galerkin's reduced order model. The flutter velocity is predicted from the solution of resultant damped eigenvalue problem. Parametric studies are conducted to know the effects of geometric factors such as taper ratio, thickness, sweep angle as well as material volume fractions and functional grading index on the flutter velocity. A generalized surrogate model is constructed by training the radial basis function network with the parametric data. The optimized material and geometric parameters of the section are predicted by solving the constrained optimal problem using firefly metaheuristics algorithm that employs the developed surrogate model for the function evaluations. The trapezoidal hollow box beam section design with axial functional grading concept is illustrated with combination of aluminium alloy and aluminium with silicon carbide particulates. A good improvement in flutter velocity is noticed by the optimization.

• Structural Engineering and Mechanics
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• v.21 no.2
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• pp.205-220
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• 2005

This paper presents the possibilities of adapting artificial neural networks (ANNs) to predict the dimensionless parameters related to the maximum contact pressures of an elasticity problem. The plane symmetric double receding contact problem for a rigid stamp and two elastic strips having different elastic constants and heights is considered. The external load is applied to the upper elastic strip by means of a rigid stamp and the lower elastic strip is bonded to a rigid support. The problem is solved under the assumptions that the contact between two elastic strips also between the rigid stamp and the upper elastic strip are frictionless, the effect of gravity force is neglected and only compressive normal tractions can be transmitted through the interfaces. A three layered ANN with backpropagation (BP) algorithm is utilized for prediction of the dimensionless parameters related to the maximum contact pressures. Training and testing patterns are formed by using the theory of elasticity with integral transformation technique. ANN predictions and theoretical solutions are compared and seen that ANN predictions are quite close to the theoretical solutions. It is demonstrated that ANNs is a suitable numerical tool and if properly used, can reduce time consumed.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.2
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• pp.191-198
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• 2009

Some of the wargame simulators currently used in the Korean Army were developed by other countries, and do not adequately reflect the Korean Peninsula terrain and weapon systems. This implies that these war game simulators need to be calibrated with respect to the input parameters for properly assessing the effectiveness of the Korean weapon systems. In this paper, AWAM, a wargame simulator, is calibrated in terms of the time-based fighting power(FP). The FP data obtained from the Korea Combat Training Center(KCTC) are used as a reference, and the differences between the AWAM and KCTC FP data are calculated at certain points in time. Then, the Taguchi robust design method is adopted using the probabilities of hitting for the K-2 rifle as controllable input parameters. Two performance characteristics are used. One is the difference between the AWAM and KCTC FP data and the other is the score derived by grouping the difference data. For each case, optimal settings of the probabilities of hitting are determined such that the mean of each characteristic is close to 0 with its dispersion being as small as possible.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.7
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• pp.832-838
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• 2003

IMO NOx levels are generally possible to meet by means of primary on-engine measures. Nevertheless further significant follow-on reductions are likely to require a secondary after-treatment technique. SCR system is currently the only available technology proven at full scale to meet the 90% NOx reduction levels. Accordingly, maybe the use of an SCR system on board ship provides the solution to minimize this primary pollutant without increasing fuel consumption. In order to develop a practical SCR system for marine application on board ship, a primary SCR system using urea was made. The SCR system was set up on the ship. employed a two-stroke diesel engine as a main propulsion. which is a training ship in KMU (Korea Maritime Univ.). The purpose of this paper is to report the results about the basic effects of the above system parameters which is investigated from practical application through its trial use. The degree of NOx removal depends on some parameters. such as the amount of urea solution added, space velocity. reaction gas temperature and activity of catalyst. The preliminary results from trial run are presented.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.7
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• pp.667-675
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• 2012

A damage estimation method for support structure of offshore wind turbine using modal parameters is presented for effective structural health monitoring. Natural frequencies and mode shapes for a support structure with monopile of an offshore wind turbine were calculated considering soil condition and added mass. A neural network was learned based on training patterns generated by the changes of natural frequency and mode shape due to various damages. Natural frequencies and mode shapes for 10 prospective damage cases were input to the trained neural network for damage estimation. The identified damage locations and severities agreed reasonably well with the accurate damages. Multi-damage cases could also be successfully estimated. Enhancement of estimation result using another parameters as input to neural network will be carried out by further study. Proposed method could be applied to other type of support structure of offshore wind turbine for structural health monitoring.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.4 s.181
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• pp.124-131
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• 2006

A gerotor pump is suitable for oil hydraulics of machine tools, automotive engines, compressors, constructions and other various applications. Especially the pump is an essential machine element of an automotive engine to feed lubricant oil. The subject of this paper is the theoretical analysis of internal lobe pump whose the main components are the rotors: usually the outer one is characterized by lobes with circular shape, while the inner rotor profile is determined as conjugate to the other. For this reason the first topic presented here is the definition of the geometry of the rotors starting from the design parameters. The choice of these parameters is subject to some limitations in odor to limit the pressure angle between the rotors. Now we will consider the design optimization. The first step is the determination of the instantaneous flow rate as a function of the design parameter. This allows us to calculate three performance indexes commonly used far the study of positive displacement pumps: the flow rate irregularity, the specific flow rate, and the specific slipping. These indexes are used to optimize the design of the pump and to obtain the sets of optimum design parameter. Results obtained from the analysis enable the designer and manufacturer of oil pump to be more efficient in this field, and the system could serve as a valuable one for experts and as a dependable training aid for beginners.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.126-131
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• 2009

This study was carried out for the reliability of PD(Partial Discharge) pattern recognition. For the pattern recognition, the database for PD was established by use of self-designed insulation defects which occur and were mostly critical in GIS(Gas Insulated Switchgear). The acquired database was analyzed to distinguish patterns by means of PRPD(Phase Resolved Partial Discharge) method and stored to the form with to unite the average amplitude of PD pulse and the number of PD pulse as the input data of neural network. In order to prove the performance of genetic algorithm combined with neural network, the neural networks with trial-and-error method and the neural network with genetic algorithm were trained by same training data and compared to the results of their pattern recognition rate. As a result, the recognition success rate of defects was 93.2% and the neural network train process by use of trial-and-error method was very time consuming. The recognition success rate of defects, on the other hand, was 100% by applying the genetic algorithm at neural network and it took a relatively short time to find the best solution of parameters for optimization. Especially, it could be possible that the scrupulous parameters were obtained by genetic algorithm.