• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.2
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• pp.12-20
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• 2007

The aiming ability is a key to improve the accuracy performance of the gun pointing system in the ground military vehicle. This paper describes the new detection method of chatter vibration using disturbance acceleration in the pointing structure. In order to analysis the vibration trends of the pointing system occurred while the vehicle driving, acceleration data obtained from vehicle was processed by using data processing algorithm with moving average and Hilbert transform. The specific mode constants of acceleration were obtained from various disturbances. Vehicle velocity, road condition and property of pointing structure were considered as factors which make the change of vibration trend in vehicle dynamics. Finally, back propagation neural networks have been applied to the pattern recognition of the classification of vibration signal in various driving conditions. Results of signal processing were compared with other condition result and analysed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.256-261
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• 2002

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1170-1175
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• 2007

This paper presents techniques to recognize music using back propagation network one of the neural network algorithms, and to preprocess technique for music mage. Music symbols and music notes are segmented by preprocessing such as binarization, slope correction, staff line removing, etc. Segmented music symbols and music notes are recognized by music note recognizing network and non-music note recognizing network. We proved correctness of proposed music recognition algorithm though experiments and analysis with various kind of musics.

• Journal of Korea Foundry Society
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• v.34 no.3
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• pp.107-111
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• 2014

Permeability is the ability of a material to transmit fluid/gases. It is an important material property and it depends on mould parameters such as grain fineness number, clay, moisture, mulling time, and hardness. Modeling the relationships among these variable and interactions by mathematical models is complex. Hence a biologically inspired artificial neural-network technique with a back-propagation-learning algorithm was developed to estimate the permeability of green sand. The developed model was used to perform a sensitivity analysis to estimate permeability. The individual as well as the combined influence of mould parameters on permeability were simulated. The model was able to describe the complex relationships in the system. The optimum process window for maximum permeability was obtained as 8.75-10.5% clay and 3.9-9.5% moisture. The developed model is very useful in understanding various interactions between inputs and their effects on permeability.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.54 no.11
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• pp.533-539
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• 2005

In the previous work, we performed the analysis of a tubular type moving-magnet linear oscillatory actuator (LOA) with cylindrical Halbach array by using 2-d analytical formulas and confirmed validity of analytical results by comparison of those with both finite element (FE) computation and experimental results. This paper deals with the dynamic characteristic analysis of the moving-magnet LOA with cylindrical Halbach array. Control parameters such as the thrust constant, the back-emf constant, resistance and inductance are obtained from both analytical and experimental results. And then, the dynamic simulation algorithm is established by the state and output equation obtained from voltage and motion equation. Finally, for various values of frequency, the dynamic simulation and experimental results for the characteristics of the voltage, current and displacement of moving-magnet LOA are presented. The simulation results are validated extensively by experiments. The experimental and simulation results for the variation of stroke according to control voltage are also presented for various values of frequency.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.6
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• pp.661-669
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• 2018

Engineering system problems consist of multi-objective optimisation and the performance analysis is generally time consuming. To optimise the system concerning its performance, many researchers perform the optimisation using an approximation model. The Response Surface Method (RSM) is usually used to predict the system performance in many research fields, but it shows prediction errors for highly nonlinear problems. To create an appropriate metamodel for marine systems, Lee (2015) compares the prediction accuracy of the approximation model, and multi-objective optimal design framework is proposed based on a confirmed approximation model. The proposed framework is composed of three parts: definition of geometry, generation of approximation model, and optimisation. The major objective of this paper is to confirm the applicability/usability of the proposed optimal design framework and evaluate the prediction accuracy based on sensitivity analysis. We have evaluated the proposed framework applicability in derrick structure optimisation considering its structural performance.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.905-910
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• 2012

This paper reports an investigation of pulse width modulation (PWM) techniques for twophase brushless DC (BLDC) motors fed by a two-phase eight-switch inverter in a fan application. The three-phase BLDC motor is widely applied in industry; however, a lower-cost two-phase BLDC motor and drive circuit has been greatly in demand in recent years. In this paper, we introduce a mathematical model of the two-phase BLDC motor with sinusoidal back electromotive forces (EMFs) based on traditional three-phase BLDC motors. To simplify the drive algorithm and speed up its application, we analyze the principle of block commutation for a two-phase BLDC motor drive in the 180-electricaldegree conduction mode, and we further propose five PWM schemes to improve the commutation performance of the two-phase BLDC drive. The effectiveness of the proposed PWM methods is verified through experiments.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.4
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• pp.523-533
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• 2000

In this paper, we present the hardware implementation of a smart antenna test-bed for a real -time performance analysis of the beam-forming algorithm operating in a wide-band CDMA environments of the WLL(Wireless Local Loop) standard. The test-bed introduced in this paper includes an external PC and signal generating module as well as the beam-forming module in order to perform, analyze, and evaluate the performance of the proposed smart antenna system. In the beam-forming module, the optimal weight vector is provided by the modified CGM algorithm. The computed weight vector is transferred back to the external PC for the performance analysis based on the off-line processing. From our analysis obtained in the hardware of the test-bed, it is concluded that the proposed smart antenna system for the WLL system is appropriate for enhancing the communication quality and capacity tremendously at the cell-site of the CDMA environment.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• Journal of the Korean Geotechnical Society
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• v.39 no.9
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• pp.13-24
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• 2023

To investigate the stability of temporary retaining walls during excavation, it is essential to develop reverse analysis technologies capable of precisely evaluating the properties of the ground and a learning model that can assess stability by analyzing real-time data. In this study, we targeted excavation sites where the C.I.P method was applied. We developed a Deep Neural Network (DNN) model capable of evaluating the stability of the retaining wall, and estimated the physical properties of the ground being excavated using a Differential Evolution Algorithm. We performed reverse analysis on a model composed of a two-layer ground for the applicability analysis of the Differential Evolution Algorithm. The results from this analysis allowed us to predict the properties of the ground, such as the elastic modulus, cohesion, and internal friction angle, with an accuracy of 97%. We analyzed 30,000 cases to construct the training data for the DNN model. We proposed stability evaluation grades for each assessment factor, including anchor axial force, uneven subsidence, wall displacement, and structural stability of the wall, and trained the data based on these factors. The application analysis of the trained DNN model showed that the model could predict the stability of the retaining wall with an average accuracy of over 94%, considering factors such as the axial force of the anchor, uneven subsidence, displacement of the wall, and structural stability of the wall.