• JSTS:Journal of Semiconductor Technology and Science
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• 제2권1호
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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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• 제10권3호
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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.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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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.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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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.

• 한국정밀공학회지
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• 제16권7호
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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.

• 한국광학회지
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• 제11권3호
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• pp.202-205
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• 2000

Er과 Yb이 첨가된 인산염 유리를 KNO3 응용액에 담구는 1단계 이온교환 공정을 통하여 채널 도파로를 제조하고자 하였다. 이온교환시 반응기 내부에 산소를 흘려줌으로서 인산염 유리의 열학한 화학적 내구성에서 비롯되는 유리 도파로 표면의 손상을 억제할 수 있었다. 제조된 도파로의 $1.5{\mu}m$ 신호광에 대한 증폭특성을 평가한 경과, 이온교환 공정 최적화 작업을 거친 45nm도파로의 경우, 2개의 980nm LD를 사용하는 양방향 펌핑시 160mW 파워에서 7.5dB의 순이득을 얻을 수 있었다.

• 한국자동차공학회논문집
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• 제19권5호
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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.

• 한국통신학회논문지
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• 제23권7호
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• pp.1718-1731
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• 1998

디지탈 quadrature 복조기는 디지탈 통신 시스템에서 변조된 신호의 정확한 위상 복조를 위해 꼭 필요하다. 기존의 방법들은 주로 DDFS(Direct Digital Frequency Synthsizer)를 이용하여 캐리어를 발생시킨 후에 승산기를 이용하여 복조를 수행하였다. 그리고, DDFS에는 주로 ROM(Read Only Memory)을 사용하였는데, 높은 속도와 정확도를 요구하는 경우 ROM의 속도와 크기가 제한이 될 수있다. 이러한 점을 극복하기 위하여 CORDIC(COordinate Rotation Digital Computer) 알고리듬을 사용하여 주파수 합성은 물론 캐리어 복조까지 수행하는 방식을 제안하였다. 최적의 하드웨어 구현을 위해 제한된 단어길이에 의한 영향을 분석하였으며, 하드웨어 비용면에서 ROM을 사용하는 방법과 비교한 결과 약 1/3 정도로 면적이 줄었다. 제안된 구조를 이요한 전주문형 VLSI 구현 결과를 보인다.

• Journal of Power Electronics
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• 제17권3호
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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.

• 대한전자공학회논문지SD
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• 제43권10호
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• pp.10-21
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• 2006

850nm oxide VCSEL의 저전류 동작 가능성을 확인하기 위하여 off 전류와 on 전류를 최대한 낮춘 상태에서 VCSEL의 특성을 살펴보았다. Oxide VCSEL의 모델링을 위해 비율 방정식을 이용하여 대신호 등가회로를 만들었고, 실험 결과와 시뮬레이션 결과의 비교를 통해 각각의 계수와 특성변수를 추출하였다. 동특성에 큰 영향을 주는 커패시턴스 성분은 C-V 미터로 측정, 분석하였다. 완성된 대신호 등가회로 모델을 이용하여 커패시턴스 성분, 그리고 on 전류와 off 전류가 turn-on 특성과 turn-off 특성, eye-diagram에 미치는 영향을 분석하였다. 그 결과 지금까지는 무시해왔던 요소인 depletion 커패시턴스가 turn-on 특성에 큰 영향을 미치고, eye-diagram에도 큰 영향을 준다는 사실을 확인하였다. 그러므로 VCSEL의 고속 동작과 저전류 동작을 동시에 구현하기 위해서는 depletion 커패시턴스를 감소시키는 공정이 필요하다.