• Journal of Intelligence and Information Systems
• /
• v.1 no.2
• /
• pp.103-121
• /
• 1995

This study proposes a artificial neural network method to predict the time to buy and sell the stocks listed on the Korea Composite Stock Price Index(KOSPI). Four types (NN1, NN2, NN3, NN4) of independent networks were developed to predict KOSPIs up/down direction after four weeks. These networks have a difference only in the length of learning period. NN5 - arithmetic average of four networks outputs - shows an higher accuracy than other network types and Multiple Linear Regression (MLR), and buying and selling simulation using systems outputs produces higher reture than buy-and-hold strategy.

• Journal of Communications and Networks
• /
• v.5 no.1
• /
• pp.65-72
• /
• 2003

Internet roundtrip delay/time (RTT) prediction plays an important role in detecting packet losses in reliable transport protocols for traditional web applications and determining proper transmission rates in many rate-based TCP-friendly protocols for Internet-based real-time applications. The widely adopted autoregressive and moving average (ARMA) model with fixed-parameters is shown to be insufficient for all scenarios due to its intrinsic limitation that it filters out all high-frequency components of RTT dynamics. In this paper, we introduce a novel parameter-varying RTT model for Internet roundtrip time prediction based on the information theory and the maximum entropy principle (MEP). Since the coefficients of the proposed RTT model are updated dynamically, the model is adaptive and it tracks RTT dynamics rapidly. The results of our experiments show that the MEP algorithm works better than the ARMA method in both RTT prediction and RTO estimation.

• Proceedings of the KIPE Conference
• /
• 2017.07a
• /
• pp.268-269
• /
• 2017

This paper presents an improved current control strategy for a three-phase grid-connected inverter under distorted grid conditions. Distorted grid condition is undesirable due to negative effects such as power losses and heating problem in electrical equipments. To enhance the power quality of distributed generation systems under such a condition, a modulated finite control set - model predictive control (MFCS-MPC) scheme will be proposed, in which the optimal switching signals of inverter are chosen by online basis using the principle of current error minimization. In addition, the moving average filter (MAF) is used to improve the phase-lock loop in order to obtain the harmonic-free reference currents on the stationary frame. The usefulness of the proposed MFCS-MPC method is proved by the comparative simulation results under different operating conditions.

• IE interfaces
• /
• v.20 no.2
• /
• pp.94-102
• /
• 2007

Portfolio management deals with decision making on 'when' and 'how' to revise an existing portfolio. In this paper, we show that a classical statistical process control (SPC) chart for normal data, a wellestablished tool in quality engineering, can effectively be used for signaling times for revising a portfolio. Noting that the day-to-day performance of a portfolio may be auto-correlated, we use the exponentially weighted moving average center-line chart to develop an automatic portfolio management procedure. The portfolio management procedure is extensively tested on historical data of equities traded in the Korea Exchange (KRX), the American Stock Exchange (AMEX), and the New York Stock Exchange (NYSE). In comparison with the performances of the KOSPI, XAX, and NYA indices during the same time periods, results from these experiments show that SPC chart-based portfolio revision presents itself a convenient and reliable method for optimally managing portfolios.

• Asia pacific journal of information systems
• /
• v.1 no.1
• /
• pp.63-86
• /
• 1991

In this paper, our concern is the artificial neural network-based patten classification, when can resolve the difficulties in the Autoregressive Moving Average(ARMA) model identification problem To effectively classify a time series into an approriate ARMA model, we adopt the Multi-layered Backpropagation Network (MLBPN) as a pattern classifier, and Extended Sample Autocorrelation Function (ESACF) as a feature extractor. To improve the classification power of MLBPN's we suggest an integrated neural network system which consists of an AR Network and many small-sized MA Networks. The output of AR Network which will gives the MA order. A step-by-step training strategy is also suggested so that the learned MLBPN's can effectively ESACF patterns contaminated by the high level of noises. The experiment with the artificially generated test data and real world data showed the promising results. Our approach, combined with a statistical parameter estimation method, will provide a way to the automation of ARMA modeling.

• Proceedings of the KIPE Conference
• /
• 2010.11a
• /
• pp.329-330
• /
• 2010

과거 볼베어링을 이용하던 하드 디스크 드라이브(HDD: Hard Disk Drive)의 스핀들 모터는 유체 동역학 베어링(FDB: Fluid Dynamic Bearing) 장착으로 저전력 소비 및 고속 구동이 가능하게 되었다. 하드 디스크 드라이브에서 스핀들 모터의 제어는 주된 읽기 쓰기 동작을 위한 가장 기본적인 제반 여건이며, 단위 면적당 데이터(BPI)가 증가함에 따라서 스핀들 모터의 제어 성능은 드라이브 성능과 직결되는 더욱 중요한 인자중의 하나가 되었다. 본 논문에서는 스핀들 모터의 샘플링 주기를 기존 대비 4배 증가하여 제어 성능 향상하는 4배속 스핀들 모터 제어 방법을 제시하고 있다.

• Current Optics and Photonics
• /
• v.3 no.3
• /
• pp.204-209
• /
• 2019

In an optical interferometer that uses sinusoidal modulation and quadrature detection, the amplitude and offset of the interference signal vary with time, even without considering system noise. As a result, the circular Lissajous figure becomes elliptical, with wide lines. We propose and experimentally demonstrate a method for compensating quadrature detection error, based on digital signal processing to deal with scaling and fitting. In scaling, fluctuations in the amplitudes of in-phase and quadrature signals are compensated, and the scaled signals are fitted to a Lissajous unit circle. To do so, we scale the average fluctuation, remove the offset, and fit the ellipse to a unit circle. Our measurements of a target moving with uniform velocity show that we reduce quadrature detection error from 5 to 2 nanometers.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.14 no.3
• /
• pp.123-131
• /
• 2019

In recent years, a non-contact respiration and heart rates monitoring via IR-UWB radar has been paid much attention to in various applications - patient monitoring, occupancy detection, survivor exploring in disaster area, etc. In this paper, we address a novel approach of real time heart rate estimation using IR-UWB radar. We apply sine fitting and peak detection method for estimating respiration rate and heart rate, respectively. We also deploy two techniques to mitigate the error caused by wrong estimation of respiration rate: a moving average filter and finding the frequency of the highest occurrence. Experimental results show that the algorithm can estimate heart rate in real time when respiration rate is presumed to be estimated accurately.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.320-322
• /
• 2013

무선센서네트워크에서 MAC 프로토콜은 듀티사이클을 이용하여 센서노드의 에너지 소비를 줄임으로써 배터리의 수명을 연장한다. 기존에 제안된 TA (Traffic-Adaptive)-MAC 프로토콜은 비동기 방식 기반으로 듀티사이클을 조절하여 센서노드의 에너지 소비를 줄인다. 본 기법은 네트워크의 트래픽 상태를 고려하여 동적으로 센서노드의 듀티사이클을 조정한다. 이러한 방법으로 센서노드의 대기시간을 줄이고 센서노드의 에너지를 효과적으로 사용한다. 하지만 이 기법은 네트워크의 트래픽 변화가 잦은 환경에서는 좋지 못한 효율을 보인다. 따라서 본 논문에서는 기존의 TA-MAC 기법에 가중이동평균 방법을 적용하여 합리적인 듀티사이클 선정을 위한 트래픽 계산 방법을 제안한다. 이는 최근 트래픽 값과 현재 감지한 트래픽의 평균을 계산하고 다음 트래픽을 예측하여 네트워크 트래픽이 급격히 변화하는 불안정한 환경에서 더 합리적인 듀티사이클 선정을 돕는다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.848-851
• /
• 2010

IEEE 802.11 DCF(Distributed Coordination Function)의 성능은 채널에 접근하기 위하여 경쟁하는 단말수에 큰 영향을 받는다. 이에 경쟁하는 단말 수를 예측하기 위하여 많은 방법들이 제안되고 있지만 기존의 방법들은 채널 오류를 고려하지 않고 있다. 따라서 본 논문에서는 기존의 제안된 방법들 중 ARMA(Auto Regressive Moving Average) 필터(Filter)가 적용된 경쟁 단말 수 예측 방법을 수정 및 개선하여 채널 오류를 반영한 단말 수 예측 방법을 제시하였다. 시뮬레이션 결과 제안된 방법은 채널 오류가 존재하는 환경에서 효과적으로 경쟁하는 단말 수를 예측할 수 있음을 확인하였다.