• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.3
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• pp.134-142
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• 2002

This paper presents a new method for the protective relaying scheme in power transformer using wavelet based neural networks. This approach is as fellows. After approximation and detail information is extracted by daub wavelet transform from differential current of power transformer, the former is used for obtaining the rate of differential currents and restrain currents, the latter used as the input of artificial neural networks to avoid the Hiss-operation in over-exciting state and magnetizing inrush state of power transformer. The simulation of EMTP with respect to different faults, inrush conditions and over-exciting conditions in power transformer have been conducted, and the results preyed that the proposed method is able to discriminate magnetizing inrush states, over-exciting stales and internal faults.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2611-2613
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• 2000

This paper deals with a novel approach to unknown inputs observer(UIO) design for linear time-invariant dynamical systems using a fast Walsh transform and Walsh function's differential operation. Generally, UIO has a derivation of system outputs which is not available from the measurement directly. And it is an obstacle to estimate the unknown inputs properly when unexpected measurement noises are presented. Therefore, this paper propose an algebraic approach to eliminate such problems by using a Walsh function's differential operation.

• Journal of IKEEE
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• v.21 no.4
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• pp.375-380
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• 2017

A differential current-to-time interval converter is presented for current mode sensors. It consists of a ramp voltage generator, a current mode sensor, a reference current source, two current-tunable Schmitt triggers, a one-shot multivibrator, and two logic gates. The design principle is to apply a ramp voltage to each input of the two current-tunable Schmitt triggers whose threshold voltages are proportional to the drain current values of the current mode sensors. A proposed circuit converts a current change in the ISFET biosensor into its equivalent pulse width change. A prototype circuit built using TSMC 0.18 nm CMOS process exhibit a conversion sensitivity amounting to $726.9{\mu}s/pH$ over pH variation range of 2-12 and a linearity error less than ${\pm}0.05%$.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.473-480
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• 2020

In this paper, we proposed a high frequency equivalent circuit considering parasitic impedance components for differential noise analysis on the input stage during DC-DC buck converter switching operation. Based on the proposed equivalent circuit model, we presented a method to measure parasitic impedance parameters included in DC bus plate, IGBT, and PCB track using the gain phase method of a network analyzer. In order to verify the validity of this model, a DC-DC prototype consisting of a buck converter, a signal analyzer, and a LISN device, and then resonance frequency was measured in the frequency range between 150 kHz and 30 MHz. The validity of the parasitic impedance measurement method and the proposed equivalent model is verified by deriving that the measured resonance frequency and the resonance frequency of the proposed high frequency equivalent model are the same.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.501-507
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• 2021

Neural networks with differentiable activation functions are differentiable with respect to input variables. We improve the approximation capability of neural networks by using the gradient and Hessian of neural networks to satisfy the differential equations of the problems of interest. We apply differential neural networks to the pricing of financial options, where stochastic differential equations and the Black-Scholes partial differential equation represent the differential relation of price of option and underlying assets, and the first and second derivatives of option price play an important role in financial engineering. The proposed neural network learns - (a) the sample paths of option prices generated by stochastic differential equations and (b) the Black-Scholes equation at each time and asset price. Experimental results show that the proposed method gives accurate option values and the first and second derivatives.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.96-108
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• 2014

In case of contrast of the image enhancement by using the conventional histogram equalization, over-enhancement, false contouring and distortion such as the details disappearance of the image occurs due to the excessive brightness change. Especially, these distortion appears when the brightness distribution is concentrated in a particular brightness level. In order to solve these problems, improved histogram equalization methods to transform the input histogram by clipping using threshold have been proposed, but contrast enhancement effect is reduced because it does not consider the characteristics of the input image's histogram to apply the same threshold for the entire histogram, and unnatural image is obtained because it does not retain the characteristics of the image. In this paper, to solve the problems of existing methods, we propose new equalization method that suppress excessive brightness changes by applying to the differential compression according to the histogram frequency, and maintain the characteristics of the input image. In addition, we propose a more effectively method to improve contrast by controlling the strength of the compression ratio depending on the characteristics of the input image.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1006-1013
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• 2011

In this paper, we propose an optimal posture control law for an unmanned bicycle by deriving linear bicycle model from fully nonlinear differential equations. We calculate each equilibrium point of a bicycle under any given turning radius and angular speed of rear wheel. There is only one equilibrium point when a bicycle goes straight, while there are a lot of equilibrium points in case of turning. We present an optimal equilibrium point which makes the leaning input minimum when a bicycle is turning. As human riders give rolling torque by moving center of gravity of a body, many previous studies use a movable mass to move center of gravity like humans do. Instead we propose a propeller as a new leaning input which generates rolling torque. The propeller thrust input makes bicycle model simpler and removes input magnitude constraint unlike a movable mass. The proposed controller can hold optimal equilibrium points using both steering input and leaning input. The simulation results on linear control for circular motion are demonstrated to show the validity of the proposed approach.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.145-149
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• 1987

The input-output relation for nonlinear systems can be explicitly represented by the Voltera functional series and it is characterized by the Volterra Kernels. A block diagram reduction method is introduced to determine the Volterra Kernels for the nonlinear systems represented by nonlinear differential equations. Degree of nonlinearity is defined and analyzed for the analysis of nonlinear systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.7
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• pp.805-810
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• 2014

Cooperative communication, which transmit signal via arbitrary number of relays, enhances the overall communication performance by providing virtual Multi-Input Multi-Output (MIMO) gain without imposing multiple antennal limitation in physical system. There are two representative relaying protocols, i.e., Amplify-and-Forward (AF) and Decode-and-Forward (DF), where we analyze the performance of cooperative communication by adopting DF relaying protocol applying partially differential modulation. The performance is based on symbol error rate (SER), and the effect of relay location on the performance is analyzed. We also compare the performance of the proposed scheme with the system which uses differential modulation scheme.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.4
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• pp.318-330
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• 2013

This paper presents a high-linearity low-noise small-size programmable gain amplifier (PGA) based on a new low-noise low-distortion differential amplifier and a proposed reconfiguration technique. The proposed differential amplifier combines an inverter-based differential pair with an adaptive biasing circuit to reduce noise and distortion. The reconfiguration technique saves the chip size by half by utilizing the same differential pair for the input transconductance and load-stage, interchangeably. Fabricated in $0.18-{\mu}m$ CMOS, the proposed PGA shows a dB-linear control range of 21dB in 16 steps from -11 dB to 10 dB with a gain error of less than ${\pm}0.33$ dB, an IIP3 of 7.4~14.5 dBm, a P1dB of -7~1.2 dBm, a noise figure of 13dB, and a 3-dB bandwidth of 270MHz at the maximum gain, respectively. The PGA occupies a chip area of $0.04mm^2$ and consumes only 1.3 mA from the 1.8 V supply.