• Ocean Systems Engineering
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• 제14권1호
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• pp.17-52
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• 2024

In this paper, the authors explore the modeling and control of a point absorber wave energy converter, which is connected to the electric grid via a power converter that is based on a linear permanent magnet synchronous generator (LPMSG). The device utilizes a buoyant mechanism to convert the energy of ocean waves into electrical power, and the LPMSG-based power converter is utilized to change the variable frequency and voltage output from the wave energy converter to a fixed frequency and voltage suitable for the electric grid. The article concentrates on the creation of a predictive control system that regulates the speed, voltage, and current of the LPMSG, and the modeling of the system to simulate its behavior and optimize its design. The predictive model control is created to guarantee maximum energy output and stable grid connection, using Matlab Simulink to validate the proposed strategy, including control side generator and predictive current grid-side converter loops.

• 한국산학기술학회논문지
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• 제10권11호
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• pp.3465-3472
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• 2009

본 병원에 축적된 의무기록과 데이터베이스에 있는 퇴원 환자 정보를 이용하여 이탈에 영향을 미치는 특성을 파악하여 활용 가능한 예측모형을 제시하고자 한다. 외래진료 방문환자 3,503명 중 충성고객 2,118명 60.5%, 이탈 고객 1,385명 39.5%을 추출하여 분석에 사용하였다. 생존한 변수는 성별, 연령(연령대), 지역, 보험구분, 입원경로, 진료과, 퇴원과, 퇴원형태, 협진여부, 수술여부, 진료예약여부, 환자구분을 기반으로 예측모형을 만들었다. 로지스틱 회귀분석을 실시한 결과 66.0%의 정확도를 나타냈고, 신경망을 통하여 예측한 결과 분석용 결과는 정분율은 69.79%이고, 검정용 결과 정분율은 63.64%였다. CHAID를 통하여 예측한 결과 분석용 결과 정분율을 83.75% 이고, 검정용 결과 정분율은 42.74%였다. 예측 모형을 활용한 이탈고객을 위한 관리와 병원의 신뢰를 높여야 할 것이다.

• 대한지리학회지
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• 제51권2호
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• pp.219-233
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• 2016

수리 또는 계량적 모형을 사용하는 사회과학연구에서 분석의 초점은 종속변수와 설명변수의 관계를 밝히는 것, 즉 설명 중심의 모형(explanatory modeling)이 지금까지 주류를 이루었다. 반면 예측(prediction) 능력 제고에 초점을 맞춘 분석은 드물었다. 본 연구에서는 이론 및 가설을 검증하거나 변수 간의 관계를 밝히는 설명 중심의 모형이 아니라 신규 관찰치에 대한 예측 오차를 줄이는, 예측 중심의 비모수 모형(non-parametric model)을 검토하였다. 서울시 강남구를 사례지역으로 선정한 후, 2011년부터 2014년까지 신고된 단독주택 실거래가를 기초자료로 하여 주택가격을 추정하였다. 적용한 비모수 모형은 기계학습 분야에서 제시된 일반가산모형(generalized additive model), 랜덤 포리스트, MARS(multivariate adaptive regression splines), SVM(support vector machines) 등이며 비교적 최근에 개발된 MARS나 SVM의 예측력이 뛰어남을 확인할 수 있었다. 마지막으로 이러한 비모수 모형에 공간적 자기상관성을 추가적으로 반영한 결과, 모형의 가격 예측력이 보다 개선되었음을 알 수 있었다. 본 연구를 계기로 그간 모수 모형에 집중되었던 부동산 가격추정 방법론이 비모수 모형으로 확대 및 다양화되기를 기대한다.

• 한국항공우주학회지
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• 제33권11호
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• pp.48-56
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• 2005

Predictive 필터는 Kalman 필터의 단점을 보완하고 모델 오차를 동시에 추정할수 있는 최근에 제시된 기법이다. 한 단계 앞의 추정 오차를 최소화하기 위한 최적화된 필터의 형태가 Predictive 필터이다. 본 필터의 주요 장점은 상태변수와 함께 모델오차를 파악할 수 있다는데 있다. 본 연구에서는 Predictive 필터를 이용한 인공위성의 자세추정 내용을 소개하도록 한다. 기존에 제시된 Predictive 필터 이론을 적용하여 자이로 바이어스 신호를 추정할수 있는 수식을 유도하고 또한 벡터 관측 정보를 이용한 자세추정 결과를 소개하도록 한다. 본 연구결과를 통해 향후 Predictive 필터의 확장 가능성을 예상할 수 있다.

• 한국간담췌외과학회지
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• 제28권1호
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• pp.14-24
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• 2024

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.62.3-62
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• 2001

The predictive control is one of the nonlinear three-axis rotation methods. The desired trace of a satellite is pre-determined, and the control inputs are designed so that the satellite follows the ´predictive´ trace. The predictive control has been adapted to the research for the three-axis attitude control. In that case, the control variables are the quaternion represented the angular rates and attitude angles of the body about the three-axes. The objective of this paper is to propose to design a predictive controller for the three-axis attitude control under external disturbances. In order to do that, this paper proposes how to construct a predictive control law including disturbances and to discern them. The basic algorithm of the existent predictive control is partially modified, and the presumption and modeling of disturbances are performed ...

• Food Science and Biotechnology
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• 제18권1호
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• pp.137-142
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• 2009

One of the most important aspects of conducting this microbial risk assessment (MRA) is determining the model in microbial behaviors in food systems. However, to fully these modeling, large expenditures or newly laboratory experiments will be spent to do it. To overcome these problems, it has to be considered to develop the new strategies that can be used data in the published literatures. This study is to show whether or not the data set from the published experimental data has more value for modeling for MRA. To illustrate this suggestion, as example of data set, 4 published Salmonella survival in Cheddar cheese reports were used. Finally, using the GInaFiT tool, survival was modeled by nonlinear polynomial regression model describing the effect of temperature on Weibull model parameters. This model used data in the literatures is useful in describing behavior of Salmonella during different time and temperature conditions of cheese ripening.

• 한국축산식품학회지
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• 제29권1호
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• pp.47-54
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• 2009

Predictive modeling was applied to study the growth of microorganisms related to spoilage in frankfurter sausage containing various levels of dietary fiber (0, 1, 2, and 3%) from rice bran and to estimate its shelf-life. Using the Baranyi model, total viable cells, anaerobic and psychrotrophic bacteria were measured during 35 days of cold storage ($<4{\pm}1^{\circ}C$). The lag times (LT) demonstrated by control and treatment groups were 6.28, 623, 6.24, and 6.25 days, respectively. The growth rate of total viable cells in each group were 0.95, 0.91, 0.92, and 0.91 (Log CFU/g/day), respectively. The anaerobic and psychrotrophic bacteria had lower initial ($y_0$) and maximal bacterial counts ($y_{max}$) than total viable cells. Also, the anaerobic and psychrotrophic bacteria possessed lower growth rate and longer lag time than total viable cells. The estimated shelf-life of frankfurter containing rice bran fiber by the growth rate of total viable cells was 7.8, 7.9, 7.9, and 7.7 days, respectively. There were no significant differences in shelf-life as a function of fiber content. In other words, the addition of dietary fiber in sausage did not show the critically hazardous results in growth of microorganism. The 12 predictive models were then characterized by high $R^2$, and small RMSE. Furthermore, $B_f$ and $A_f$ values showed a very close relationship between the predictive and observed data.

• 한국공작기계학회논문집
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• 제15권2호
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• pp.66-72
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• 2006

The old technique of sandblasting which has been used for paint or scale removing, deburring and glass decorating has recently been developed into a powder blasting technique for brittle materials, capable of producing micro structures larger than $100{\mu}m$. In this paper, we studied on the predictive modeling of material removal and surface roughness in powder blasting of glass by design of experiments. The surface characteristics and surface shape of powder blasted glass surface were tested under different blasting parameter. Finally, we proposed a predictive model for powder blasting process, and compared with experimental results.

• Nuclear Engineering and Technology
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• 제37권2호
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• pp.177-184
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• 2005

The Multivariate State Estimation Technique (MSET) is being used in Nuclear Power Plants for sensor and equipment condition monitoring. This paper presents the use of regularization methods for optimizing MSET's predictive performance. The techniques are applied to a simulated data set and a data set obtained from a nuclear power plant currently implementing empirical, on-line, equipment condition monitoring techniques. The results show that regularization greatly enhances the predictive performance. Additionally, the selection of prototype vectors is investigated and a local modeling method is presented that can be applied when computational speed is desired.