• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2004.11a
• /
• pp.999-1004
• /
• 2004

In this study, we identify contribution of structure-borne-noise of vehicle HVAC system using Multi-Dimensional spectral analysis (MDSA) method. Firstly, to identify the applicability of MDSA method, the case of HVAC system was modeled with four input / single output system. The four inputs which is given vibration data is composed of blower, evaporator, heater and duct. The single output is noise data from driver's seat. When the blower motor is operating, we analyze the contributions of four input / single output. As a result of experiment, we identify efficiency of systems modeled with four input / single output through ordinary coherence function (OCF) and multiple coherence function (MCF).

• Journal of Power Electronics
• /
• v.10 no.2
• /
• pp.115-124
• /
• 2010

In this paper, a simple yet efficient auto mode-hop ripple control structure for buck converters with light load operation enhancement is proposed. The converter, which operates under a wide range of input and output voltages, makes use of a state-dependent hysteretic comparator. Depending on the output current, the converter automatically changes the operating mode. This improves the efficiency and reduces the output voltage ripple for a wide range of output currents for given input and output voltages. The sensitivity of the output voltage to the circuit elements is less than 14%, which is seven times lower than that for conventional converters. To assess the efficiency of the proposed converter, it is designed and implemented with commercially available components. The converter provides an output voltage in the range of 0.9V to 31V for load currents of up to 3A when the input voltage is in the range of 5V to 32V. Analytical design expressions which model the operation of the converter are also presented. This circuit can be implemented easily in a single chip with an external inductor and capacitor for both fixed and variable output voltage applications.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1987-1992
• /
• 2004

A Variable Structure Model Reference Adaptive Controller (VS-MRAC) using state Variables is proposed for single input multi output systems. . The structure of the switching functions is designed based on stability requirements, and global exponential stability is proved. Transient behavior is analyzed using sliding mode control and shows perfect model following at a finite time. The effect of input disturbances on stability and transients is investigated and shows preference to the conventional MRAC schemes with integral adaptation law. Sliding surfaces are independent of system parameters and therefore VS-MRAC is insensitive to system parameter variations. Simulation is presented to clear the theoretical results.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.36 no.7
• /
• pp.484-491
• /
• 1987

A sufficent condition for the robust control of the adaptive control system is presented under the convergence of the parameters of the adaptive system. The plant in the adaptive control system is a stable system which includes the unmodelled dynamics and can be approximated by a minimum phase system. It is shown that modified structure which Kosut and Friedlander suggested satisfies the sufficient condition more easily than the original structure without modification. It is also shown by computer simulation that the modified structure and/ or the adaptation method using the normalized input and output data or filtered input and output data can improve the robustness of the adaptive control system.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.4
• /
• pp.832-839
• /
• 2009

In this paper, we introduce an design of fuzzy relation-based fuzzy neural networks with multi-output. Fuzzy relation-based fuzzy neural networks comprise the network structure generated by dividing the entire input space. The premise part of the fuzzy rules of the network reflects the relation of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions such as constant, linear, and modified quadratic. For the multi-output structure the neurons in the output layer were connected with connection weights. The learning of fuzzy neural networks is realized by adjusting connections of the neurons both in the consequent part of the fuzzy rules and in the output layer, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, learning rate and momentum coefficient are automatically optimized by using real-coded genetic algorithm. Two examples are included to evaluate the performance of the proposed network.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.29 no.10
• /
• pp.17-24
• /
• 2015

In this paper, we investigated the output polarization of a Lyot-type optical fiber comb filter based on a polarization-diversity loop structure. It was found that the output state of polarization (SOP) of the filter made a wavelength-dependent evolution, and the spectral periods of the output SOP variation in flat-top and lossy flat-top band modes were the channel separation of the filter and its half, respectively. For a certain input SOP, the filter could pass or reject specific spectral sections by adding and controlling an output analyzer. In particular, it was theoretically anticipated that the filter with the output polarizer could provide the fine continuous tuning of its pass band center in a wavelength range corresponding to the ${\pm}9.5%$ of channel spacing(0.8nm) when the input SOP was properly adjusted. It is expected that this tuning function can be effectively applied to suppress unwanted spectral portions in modulated optical signals.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10b
• /
• pp.168-173
• /
• 1987

The optimal Input design problem for linear system Which have the common parameters in the system and noise transfer functions. Exploiting the assumed Model structure and deriving the information matrix structure in detail, D-optimal open-loop stochastic input can be realized as an ARMA process under the Input or output variance constraints. In spite of the reduced order, It Is necessary to develop an efficient algorithms for the optimation with respect to the .rho..

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.433-439
• /
• 1998

In this paper, we present an approach to the structure identification based on the input space partition methods and to the parameter identification by hybrid learning method in neuro-fuzzy system. The structure identification can automatically estimate the number of membership function and fuzzy rule using grid partition, tree partition, scatter partition from numerical input-output data. And then the parameter identification is carried out by the hybrid learning scheme using back-propagation and least squares estimate. Finally, we sill show its usefulness for neuro-fuzzy modeling to truck backer-upper control.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.8
• /
• pp.689-696
• /
• 2000

In this paper we propose an eminent controller for wheeled mobile robots. This controller consists of an input-output linearization controller trying to stabilize the system and a neural network controller to compensate for uncertainties. The uncertainties are divided into two parts. First unstructured uncertainties include the elements related with system order such as friction disturbance. Second structure uncertainties are the incorrect system parameters A neural network structure of the proposed overall controller learns structural errors of the wheeled mobile robots with uncertainties and includes the neural network output. This controller learns quickly the model and has good tracking performance Simulation results show that the proposed controller is more efficient than analog controllers.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2000.11a
• /
• pp.343-346
• /
• 2000

This paper presents the characteristics of low consumption, high-response time hot-film type micro-flowsensors with SOI(Si-on-insulator) and trench structures. Output voltages increased due to increase of heat-loss from sensor to external. Compared with no-trench on the SOI structure, the micro-flowsensors with trench structures have properties of high output voltage and low consume power. Output voltage of micro-flowsensors with SOI and trench structures was 250 mV at $N_2$ flow rate of 2000 sccm/min, heating power of 0.3 W. The response time was about 85 msec when input flow was step-input.