• Journal of Power Electronics
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• 제17권6호
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• pp.1587-1599
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• 2017

This paper proposes a real-time implementation of an optimal operation of a double stage grid connected wind power system incorporating an active power filter (APF). The system is used to supply the nonlinear loads with harmonics and reactive power compensation. On the generator side, a new adaptive neuro fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) control is proposed to track the maximum wind power point regardless of wind speed fluctuations. Whereas on the grid side, a modified predictive current control (PCC) algorithm is used to control the APF, and allow to ensure both compensating harmonic currents and injecting the generated power into the grid. Also a type 2 fuzzy logic controller is used to control the DC-link capacitor in order to improve the dynamic response of the APF, and to ensure a well-smoothed DC-Link capacitor voltage. The gained benefits from these proposed control algorithms are the main contribution in this work. The proposed control scheme is implemented on a small-scale wind energy conversion system (WECS) controlled by a dSPACE 1104 card. Experimental results show that the proposed T2FLC maintains the DC-Link capacitor voltage within the limit for injecting the power into the grid. In addition, the PCC of the APF guarantees a flexible settlement of real power exchanges from the WECS to the grid with a high power factor operation.

• ETRI Journal
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• 제26권4호
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• pp.297-306
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• 2004

A plasma is a collection of charged particles and on average is electrically neutral. In fabricating integrated circuits, plasma etching is a key means to transfer a photoresist pattern into an underlayer material. To construct a predictive model of plasma-etching processes, a polynomial neural network (PNN) is applied. This process was characterized by a full factorial experiment, and two attributes modeled are its etch rate and DC bias. According to the number of input variables and type of polynomials to each node, the prediction performance of the PNN was optimized. The various performances of the PNN in diverse environments were compared to three types of statistical regression models and the adaptive network fuzzy inference system (ANFIS). As the demonstrated high-prediction ability in the simulation results shows, the PNN is efficient and much more accurate from the point of view of approximation and prediction abilities.

• 한국조사연구학회지:조사연구
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• 제9권1호
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• pp.87-100
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• 2008

200여개의 지역구 선거가 동시에 치러지는 국회의원 선거에서 주요정당의 의석 수를 예상해야 할 필요가 있는데, 이제까지는 정당별로 당선확실 선거구 수에 경합 선거구 수를 적당히 더하는 상식적 수준의 셈에 의존하여 왔다. 그러나 선거 예측 조사 자료를 베이즈 추론의 틀에 넣어 활용함으로써 정당 의석 수에 대한 합리적 점 예측과 구간 예측이 가능하다. 2004년의 제 17대 국회의원 선거에 적용하여 이 방법의 실용성을 살펴보았다.

• Journal of the Korean Data and Information Science Society
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• 제10권1호
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• pp.119-133
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• 1999

마코브체인 몬테칼로 방법을 소프트웨어 신뢰모형에 이용하였다. 베이지안 추론에서 조건부 분포를 가지고 사후분포를 결정하는데 있어서의 계산 문제를 고찰하였다. 특히 레코드값을 통계량을 갖고서 혼합과정과 중첩과정에 대하여 깁스샘플링 알고리즘과 메트로폴리스 알고리즘을 활용하여 베이지안 계산과 모형 선택을 제시하고 모의실험자료를 이용하여 수치적 인 계산을 시행하고 그 결과를 비교하였다.

• Ocean Science Journal
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• 제42권2호
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• pp.69-79
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• 2007

A vertical transect with 4 km length was established for the macrofaunal survey on the Chokchon macrotidal flat in Kyeonggi Bay, Incheon, Korea, 1994. Tidal elevation (m) and sediment mean grain size $(\phi)$ were inversely predicted by the transfer functions from the faunal assemblages. Three methods: weighted average using optimum value (WA), tolerance weighted version of the weighted average (WAT) and maximum likelihood calibration (MLC) were employed. Estimates of tidal elevation and mean grain size obtained by using the three different methods showed positively corresponding trends with the observations. The estimates of MLC were found to have the minimum value of sum of squares due to errors (SSE). When applied to the previous data $(1990\sim1992)$, each of three inference models exhibited high predictive power. This result implied there are visible relationships between species composition and faunas' critical environmental factors. Although a potential significance of the two major abiotic factors was re-affirmed, a weak tendency of biological interaction was detected from faunal distribution patterns across the flat. In comparison to the spatial and temporal patterns of the estimates, it was suggested that sediment characteristics were the primary factors regulating the distribution of macrofaunal assemblages, rather than tidal elevation, and the species composition may be sensitively determined by minute changes in substratum properties on a tidal flat.

• Asian Pacific Journal of Cancer Prevention
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• 제15권9호
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• pp.4049-4054
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• 2014

Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and compare goodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the Markov Chain Monte Carlo technique to determine the posterior density function of the parameters. After evaluating the model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, we derived the predictive survival density for future survival time and its related inferences. Results: The analytical sample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009). The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and the mean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggested that the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females' breast cancer survival data. This model predicted the survival times (in months) for White non-Hispanic women after implementation of precise estimates of the model parameters. Conclusions: By using modern model building criteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimates of the parameter into the predictive model and evaluated the survival inference for the White non-Hispanic female population. This method of analysis will assist researchers in making scientific and clinical conclusions when assessing survival time of breast cancer patients.

• International Journal of Control, Automation, and Systems
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• 제2권1호
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• pp.39-54
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• 2004

We developed an automatic blood pressure control system to maintain the blood pressure of patients at a substantially low level during a surgical operation. The developed system discharges two functions, continuous feedback control of the mean arterial pressure (MAP) by a state-predictive servo controller and risk control based on the inference by fuzzy-like logics and rules using measured data. Twenty-eight clinical applications were made beginning in November 1995, and the effects of the automatic blood pressure control on the operation time and on bleeding were assessed affirmatively by means of Wilcoxon testing. This paper essentially reports the engineering details of the control system.

• Journal of the Korean Data and Information Science Society
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• 제27권3호
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• pp.803-814
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• 2016

Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.

• Journal of Multimedia Information System
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• 제6권2호
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• 한국지능시스템학회논문지
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• 제7권2호
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• pp.43-50
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• 1997

쓰러기 소각로는 다음과 같은 불명확한 요소들을 포함한다. 즉 연료로 사용되는 쓰레기의 물리적 특성의변동 그리고 연소현상의 복잡성 등이다. 이것은 기존의 제어기법을 쓰레기의 연소제어에 적용하기가 매우 어렵게 만든다. 따라서 대부분의 쓰레기 소각로는 조작자의 운전에 의존한다. 본 논문에서는 쓰레기 소각로의 연소제어를 위한 다변수퍼지모델 예측제어를 제안한다. 쓰레기 소각로의 모델을 구하기 위해 적응 네트워크에 기초한 퍼자추론시스템이 사용되고 동정된 퍼지 모델을 이용하여 다변수 퍼지모델 예측제어기가 설계된다. 그리고 제안된 제어기의 성능을 평가하기 위해 컴퓨터 시뮬레이션이 수행되었다.