• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.383-388
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• 2014

This paper describes a wave modeling method using low-cost sensors. Wave modeling is applied to the wave monitoring system for accurate measurement of ocean wave parameters. The observation of ocean wave parameters is necessary to improve the accuracy of forecast of ocean wave condition. However, the ocean wave parameters measured by a low-cost wave monitoring system suffer from several errors. Therefore we introduce a wave modeling method to compensate the ocean wave parameters corrupted by errors. The proposed method is analyzed using experiments within controlled environment. It is verified that the accuracy of low-cost wave monitoring system can be increased by the proposed method.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.485-493
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• 2007

This paper deals with the problem of detection of induction motor incipient faults. Artificial Neural Network (ANN) approach is applied to detect two types of incipient faults (1). Interturn insulation and (2) Bearing wear faults in single-phase induction motor. The experimental data for five measurable parameters (motor intake current, rotor speed, winding temperature, bearing temperature and the noise) is generated in the laboratory on specially designed single-phase induction motor. Initially, the performance is tested with two inputs i.e. motor intake current and rotor speed, later the remaining three input parameters (winding temperature, bearing temperature and the noise) were added sequentially. Depending upon input parameters, the four ANN based fault detectors are developed. The training and testing results of these detectors are illustrated. It is found that the fault detection accuracy is improved with the addition of input parameters.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.135-142
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• 2008

This paper deals with the design of a robust PI controller for a vehicle suspension system. A method, which is related to computation of all stabilizing PI controllers, is applied to the vehicle suspension system in order to obtain optimum control between passenger comfort and driving performance. The PI controller parameters are calculated by plotting the stability boundary locus in the $(k_p,\;k_i)$-plane and illustrative results are presented. In reality, like all physical systems, the vehicle suspension system parameters contain uncertainty. Thus, the proposed method is also used to compute all the parameters of a PI controller that stabilize a vehicle suspension system with uncertain parameters.

• ETRI Journal
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• v.28 no.2
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• pp.182-190
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• 2006

The circuit modeling of interdigitated capacitors fabricated by high-k low-temperature co-fired ceramic (LTCC) sheets was investigated. The s-parameters of each test structure were measured from 50 MHz to 10 GHz, and the modeling was performed using these measured sparameters up to the first resonant frequency. Each test structure was divided into appropriate building blocks. The equivalent circuit of each building block was composed based on the partial element equivalent circuit (PEEC) method. Modeling was executed to optimize the parameters in the equivalent circuit of each building block. The validity of the extracted parameters was verified by the predictive modeling for the test structures with different geometry. After that, Monte Carlo analysis and sensitivity analysis were performed based on the extracted parameters. The modeling methodology can allow a device designer to improve the yield and to save time and cost for the design and manufacturing of devices.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.44-51
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• 1998

Two-step neural network is designed for determining electrical discharge machining parameters in low erosion. The first neural network, which is used as a classification network, checks whether the current conditions are appropriate to electrical discharge machining in low tool erosion. If the conditions are appropriate to EDM in low erosion, suitable EDM parameters are generated by the second neural network. Theoretically known EDM conditions are produced and also utilized for training the second neural network. The trained neural network is tested how well suitable EDM machining conditions are generated under unknown machining situations Experimental result shows that the proposed two-step neural network approach could be effectively used for determining EDM parameters in low tool erosion. The results also have a practical contribution to EDM area in that it could be applied for maintaining low tool wear as well as obtaining maximum machining rates simultaneously.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.350-356
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• 2005

Earth equipments are essential to protect humans and other types of equipment from abnormal conditions. Earth resistance and potential must be restricted within a low value. An estimation algorithm of earth parameters and equivalent resistivity is introduced to calculate reliable earth resistance in this research. The proposed algorithm is based on the relationship between apparent resistances and earth parameters. The proposed algorithm, which approximates the non-linear characteristics of earth by using the Artificial Neural Network (ANN), estimates the earth parameters and equivalent resistivity. The effectiveness of the proposed method is verified with case studies.

• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.613-616
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• 2002

Luminance and contrast ratio is one of the most fundamental and important parameters of plasma display panel. Understanding the effects of cell design parameters on the display and background luminance is inevitable for improving the contrast ratio. We report the experimental results on the relationships between cell parameters and contrast ratio of the ac PDP driven by ADS scheme. It was found that the contrast ratio is the most significantly affected by rib height and optimum range of the rib height simultaneously affects the facing discharge during the reset periods, diffusion loss of plasma and shadowing of the visible light emitted from phosphor.

• JSTS:Journal of Semiconductor Technology and Science
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• v.9 no.1
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• pp.61-66
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• 2009

We have investigated the noise sensitivity of low noise amplifier (LNA) at different frequency. This noise sensitivity analysis provides insights about noise parameters and it is very beneficial for making appropriate design trade-offs. From this work, the circuit designer can choose the adequate noise parameters tolerances.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.5
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• pp.43-49
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• 1998

Adaptive neural network approach is presented for determining Electrical Discharge Machining (EDM) parameters. Electrical Discharge Machining has been widely used with its capability of machining hard metals and tough shapes. In the past few years, EDM has been established in tool-room and large-scale production. However. in spite of it's wide application, an universal selection method of EDM parameters has not been established yet. No attempt has been tried before to suggest a logical method in determining essential machine parameters considering the machining rate and resulting surface roughness integrity. The paper presents a method, which is focusing on determining appropriate machining parameters. Depending on the electrode wear and surface roughness, an adaptive neural network is designed for providing suitable machining guideline.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.101-106
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• 2008

In the wire cut electrical discharge machining, the optimal machining parameters setting satisfying the requirements of both high efficiency and good quality is very difficult because its process involves a series of complex physical phenomena and the machining parameters are numerous over diverse range. In this paper, the experimental investigation has been performed to find out the influence of the machining parameters on the machining performance such as cutting speed and surface roughness. The selected experimental parameters are no load voltage, discharge peak current and pulse-off time. The experimental results give the guideline for selecting suitable machining parameters.