• Proceedings of the KIPE Conference
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• 2001.12a
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• pp.129-132
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• 2001

Recently, a ZVT boost converter is embedded in a power factor correction system. The control circuit of the converter assures soft-switching for all the MOSFETs and load regulation. The PFC system contains additional control circuits which assure the input voltage in a sinusoidal form and feed-forward line voltage regulation. In this paper, a soft switching boost converter with zero-voltage transition(ZVT) main switch using zero-current switching(ZCS) auxiliary switch is proposed. Operating intervals of the converter are persented and analyzed. The proposed results show that the main switch maintains UT while auxiliary switch retains ZCS for the complete specified line and load conditions.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.812-814
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• 1999

In this paper, we propose a new control algorithm based on the neural network(NN) feedback compensation with a desired trajectory modification. The proposed algorithm decreases trajectory errors by a feed-forward desired torque combined with a neural network feedback torque component. And, to robustly control the tracking error, we modified the desired trajectory by variable structure concept smoothed by a fuzzy logic. For the numerical simulation, a 2-link robot manipulator model was assumed. To simulate the disturbance due to the modelling uncertainty. As a result of this simulation, the proposed method shows better trajectory tracking performance compared with the CTM and decreases the chattering in control inputs.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.3
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.1106-1111
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• 2007

This paper is focused on design of a new active control engine mount (ACM), which is compact in size and cost effective. The ACM, consisting of an electrodynamic actuator as the active element, flat springs and a sliding ball joint, is different in structure from the previous ACM designs based on the conventional hydraulic engine mount. Dynamic characteristics of the proposed ACM are extensively investigated before a prototype ACM, which meets the design specifications, is built in the laboratory. For cost effectiveness, a feed-forward control algorithm without a feedback sensor is used for reduction of the transmitted force through the ACM from the engine. The prototype ACM is then harmonic-tested with a rubber testing machine for verification of its control performance as well as adequacy of modeling. Experimental results show that the proposed ACM is capable of reducing the transmitted force by 20 dB up to the frequency range of 60 Hz.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.71-94
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• 2009

In this paper a procedure for Monte Carlo simulation of univariate stationary stochastic processes with the aid of neural networks is presented. Neural networks operate model-free and, thus, circumvent the need of specifying a priori statistical properties of the process, as needed traditionally. This is particularly advantageous when only limited data are available. A neural network can capture the "pattern" of a short observed time series. Afterwards, it can directly generate stochastic process realizations which capture the properties of the underlying data. In the present study a simple feed-forward network with focused time-memory is utilized. The proposed procedure is demonstrated by examples of Monte Carlo simulation, by synthesis of future values of an initially short single process record.

• Computers and Concrete
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• v.8 no.5
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• pp.491-510
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• 2011

Before deciding if structures exposed to high temperature are to be repaired or demolished, their final state should be carefully examined. Destructive and non-destructive testing methods are generally applied for this purpose. Compressive strength and color change in mortars are observed as a result of the effects of high temperature. In this study, ordinary and pozzolan-added mortar samples were produced using different aggregates, and exposed to 100, 200, 300, 600, 900 and $1200^{\circ}C$. The samples were divided into two groups and cooled to room temperature in water and air separately. Compression tests were carried out on these samples, and the color change was evaluated by the Munsell Color System. The relationships between the change in compressive strength and color of mortars were determined by using a multi-layered feed-forward Neural Network model trained with the back-propagation algorithm. The results showed that providing accurate estimates of compressive strength by using the color components and ultrasonic pulse velocity design parameters were possible using the approach adopted in this study.

• International Journal of Automotive Technology
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• v.7 no.5
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• pp.547-554
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• 2006

This paper is concerned with an optimal control suspension system using the preview information of road input based on a quarter car model. The main purpose of the control is to combine good vibration isolation characteristics with improved attitude control. The optimal control law is derived with the use of calculus of variation, consisting of three parts. The first part is a full state feedback term that includes integral control acting on the suspension deflection to ensure zero steady-state deflection in response to static body forces and ramp road inputs. The second part is a feed-forward term which compensates for the body forces when they can be detected, and the third part depends on previewed road input. The performance of the suspension is evaluated in terms of frequency domain characteristics and time responses to ramp road input and cornering forces. The effects of each part of the suspension controller on the system behavior are examined.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.3
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• pp.244-249
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• 2008

In this paper, the stable region for missile guidance loop employing an integrated proportional navigation guidance law is derived. The missile guidance loop is formulated as a closed-loop control system consisting of a linear time-invariant feed-forward block and a time-varying feedback gain. By applying the circle criterion to the system, a bound for the time of flight up to which stability can be assured is established as functions of flight time. Less conservative results, as compared to the result by Popov criterion, are obtained.

• Structural Engineering and Mechanics
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• v.26 no.4
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• pp.377-392
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• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.09a
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• pp.325-333
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• 1995

Back-propagation is the most widely used algorithm for supervised learning in multi-layer feed-forward networks. However, back-propagation is very slow in convergence. In this paper, a new weight initialization method, called rough map initialization, in multi-layer perceptrons is proposed. To overcome the long convergence time, possibly due to the random initialization of the weights of the existing multi-layer perceptrons, the rough map initialization method initialize weights by utilizing relationship of input-output features with singular value decomposition technique. The results of this initialization procedure are compared to random initialization procedure in encoder problems and xor problems.