• JSTS:Journal of Semiconductor Technology and Science
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• v.2 no.1
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• pp.59-69
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• 2002

A semi-analytical model based on exact numerical analysis of the 2DEG channel in pseudo-morphic HEMT (PMHEMT) is presented. The exactness of the model stems from solving both Schrodinger's wave equation and Poisson's equation simultaneously and self-consistently. The analytical modeling of the device terminal characteristics in relation to the charge control model has allowed a best fit with the geometrical and structural parameters of the device. The numerically obtained data for the charge control of the channel are best fitted to analytical expressions which render the problem analytical. The obtained good agreement between experimental and modeled current/voltage characteristics and small signal parameters has confirmed the validity of the model over a wide range of biasing voltages. The model has been used to compare both the performance and characteristics of a PMHEMT with a competetive HEMT. The comparison between the two devices has been made in terms of 2DEG density, transfer characteristics, transconductance, gate capacitance and unity current gain cut-off frequency. The results show that PMHEMT outperforms the conventional HEMT in all considered parameters.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.3
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• pp.169-177
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• 2021

In this paper, we propose a novel global navigation satellite system (GNSS) spoofing detection technique through an array antenna-based direction of arrival (DoA) estimation of satellite and spoofer. Specifically, we consider a sophisticated GNSS spoofing attack scenario where the spoofer can accurately mimic the multiple pseudo-random number (PRN) signals since the spoofer has its own GNSS receiver and knows the location of the target receiver in advance. The target GNSS receiver precisely estimates the DoA of all PRN signals using compressed sensing-based orthogonal matching pursuit (OMP) even with a small number of samples, and it performs spoofing detection from the DoA estimation results of all PRN signals. In addition, considering the initial situation of a sophisticated spoofing attack scenario, we designed the algorithm to have high spoofing detection performance regardless of the relative spoofing signal power. Therefore, we do not consider the assumption in which the power of the spoofing signal is about 3 dB greater than that of the authentic signal. Then, we introduce design parameters to get high true detection probability and low false alarm probability in tandem by considering the condition for the presence of signal sources and the proximity of the DoA between authentic signals. Through computer simulations, we compare the DoA estimation performance between the conventional signal direction estimation method and the OMP algorithm in few samples. Finally, we show in the sophisticated spoofing attack scenario that the proposed spoofing detection technique using OMP-based estimated DoA of all PRN signals outperforms the conventional spoofing detection scheme in terms of true detection and false alarm probability.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.7
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• pp.15-23
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• 1999

With a remarkable expansion of communication industry, a pager or a cellular phone becomes a necessary communication device in modern society. However, a paging signal by a buzzer is often acted as an unpleasant noise in some places, thus necessitating a paging signal by a vibration motor. In this paper, a simpler type of a vibration motor, a coreless permanent magnet(PM) DC motor, is considered to substitute for the conventional vibration motors. Using an analytical method, electromagnetic field and operating torque were calculated for the given inner and outer PM type motors, and the results were confirmed by FEM analysis. As design parameters, number of PM poles, PM radial thickness, coil arc angle and number of winding stacks were chosen for sensitivity analysis. It shows that coil arc angle is the most important design parameter to increase the motor performance, without giving an adverse effect on motor weight, size and manufacturing cost. Based on the analysis of the outer PM type motor, an outer square PM type motor is proposed as the final design. Compared to the outer PM type, outer square type provides more flexibility to attach to the small size cellular phones. With the optimum design of square outer PM DC motor, it can successfully substitute the conventional types with less expensive manufacturing cost. better performance and smaller necessary space.

• Korean Journal of Optics and Photonics
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• v.11 no.3
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• pp.202-205
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• 2000

One step thermal potassium ion exchange process was carried out to form optical channel waveguides in an ErNb co-doped phosphate glass. Flowing oxygen gas into KN03 melt during ion exchange was effective to prevent glass surface damage that causes an increase of waveguide propagation loss. Amplification characteristics of the waveguides were evaluated at $1.5{\mu}m$ signal wavelength with 980 om laser diode pump. A 45 mm long waveguide whose processing parameters had been optimized exhibited a small signal net gain of 7.5 dB at the launched pump power of 160 mW.160 mW.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.5
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• pp.45-50
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• 2011

Compression pressure of individual cylinder and valve timing have big influence on combustion pressure, indicated mean effective pressure (IMEP), emission, vibration, combustion noise and many other combustion parameters. Conventional method, however, to check compression pressure uniformity is done by mechanical pressure gage and valve timing is checked manually. This conventional method causes inaccuracy of cylinder pressure measurement because of different cranking speed results from battery status and temperature. Also to check valve timing, related FEAD parts should be disassembled and timing mark should be checked. This study describes and suggests new methodology to measure compression pressure by analysis of start motor current and to check valve timing by cylinder pressure with high accuracy. Results, it is found that detection of bulky as well as small leaky cylinder is possible by cranking motor current analysis and wrong valve timing can be detected by cylinder pressure analysis and cam and crank sensor signal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.7
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• pp.1718-1731
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• 1998

Digital quadrature demodulator is needed for the coherent demodulation in the digital communication systems such as Binary Phase-Shift-Keying, Quadrature Phase-Shift-Keying, and Quadrature Anmplitude Modulation. Conventaionally, the DDFS (Direct Digital Frequency Synthsizer) is used for generating the carrier signal and seperate multi-pliers are used for mixing. And the DDFS is implemented using the ROM (Read Only Memory), which can be a bottle-neck neck when the fast-speed and small-area implementation is required. A new architecture is developed, which employs the circular rotation mode of the CORDIC algorithm for signal mixing as well as carrier generation. To optimize the hardware design parameters, the finiteword-length effects of the proposed implementation arachitecture are analyzed in comparison with a conventional ROM-based architecture. The hardware costs are also estimated, which showed that the proposed architecture occupies only a third of the area of the conventional ROM-based architecture for the same performance. A full-custom VLSI is developed using the proposed architecture.

• Journal of Power Electronics
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• v.17 no.3
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• pp.704-715
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• 2017

For three-phase synchronization applications, the synchronous reference frame phase-locked loop (SRF-PLL) is probably the most widely used technique due to its ease of implementation and satisfactory phase tracking performance under ideal grid conditions. However, under unbalanced and distorted grid conditions, its performance tends to worsen. To deal with this problem, a variety of filtering stages have been proposed and used in SRF-PLLs for the rejection of disturbance components at the cost of degrading the dynamic performance. In this paper, to improve dynamic performance without compromising the filtering capability, an effective hybrid filtering stage is proposed and incorporated into the inner loop of a quasi-type-1 PLL (QT1-PLL). The proposed filtering stage is a combination of a moving average filter (MAF) and a modified delay signal cancellation (DSC) operator in cascade. The time delay caused by the proposed filtering stage is smaller than that in the conventional MAF-based and DSC-based PLLs. A small-signal model of the proposed PLL is derived. The stability is analyzed and parameters design guidelines are given. The effectiveness of the proposed PLL is confirmed through experimental results.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.10 s.352
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• pp.10-21
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• 2006

We have studied the characteristics of oxide VCSELS when their off-current and on-current are kept small in order to find out the possibility of low current operation. A large signal equivalent circuit model has been used. By comparing measured data and simulation results, the parameters of the large signal models are obtained including the capacitances. Using the large signal model, we have investigated the effects of capacitance and on/off currents upon the turn-on/turn-off characteristics and eye diagram. According to the experiment and simulation, the depletion capacitance, which has been neglected, is found to have significant influence on the him-on delay and eye-diagram. Therefore, for high speed and low current operation, the reduction of the depletion capacitance is essential.