• 기본간호학회지
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• 제19권4호
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• pp.444-452
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• 2012

Purpose: To explore the usefulness of previous fall history as a triage variable for inpatients. Methods: Medical records of 21,382 patients, admitted to medical units of one tertiary hospital, were analyzed retrospectively. Inpatient falls were identified from the hospital's self-report system. Non-falls in 1,125 patients were selected by a stratified matching sampling with 125 patients with falls (0.59%). A comparative and predictive accuracy analysis was conducted to describe differences between the two groups with and without a history of falls. Logistic regression was used to measure the effect size of the fall history. Results: The fall history group showed higher prevalence by 9 fold than the non-fall history group. The relationships between falls and relevant variables which were significant in the non-fall history group, were not significant for the fall history group. Falls in the fall history group were 25 times more likely than in the non-fall group. Predictive accuracy of the risk assessment tool showed almost zero specificity in the fall history group. Conclusion: The presence of fall history, the fall prevalence, variables relevant to falls, and the accuracy of the risk tool were different, which support the usefulness of the fall history as a triage variable.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.45-48
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• 1999

Model predictive control algorithm requires a relevant model of the system to be controlled. Unfortunately, the first principle model describing a polymerization reaction system has a large number of parameters to be estimated. Thus there is a need for the identification and control of a polymerization reactor system by using available input-output data. In this work, the polynomial auto-regressive moving average (ARMA) models are employed as the input-output model and combined into the nonlinear model predictive control algorithm based on the successive linearization method. Simulations are conducted to identify the continuous styrene polymerization reactor system. The input variables are the jacket inlet temperature and the feed flow rate whereas the output variables are the monomer conversion and the weight-average molecular weight. The polynomial ARMA models obtained by the system identification are used to control the monomer conversion and the weight-average molecular weight in a continuous styrene polymerization reactor It is demonstrated that the nonlinear model predictive controller based on the polynomial ARMA model tracks the step changes in the setpoint satisfactorily. In conclusion, the polynomial ARMA model is proven effective in controlling the continuous styrene polymerization reactor.

• 펄프종이기술
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• 제35권4호
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• pp.48-56
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• 2003

In this work an integrated model for paper plants combining wet-end and dry section is developed and a model predictive control scheme based on the plant model is proposed. Closed-loop process identification method is employed to produce a state-space model. Thick stock, filler flow, machine speed and steam pressure are selected as input variables and basis weight, ash content and moisture content are considered as output variables. The desired output trajectory is constructed in the form of 1st-order dynamics. Results of simulations for control of grade change operations are compared with plant operation data collected during the grade change operations under the same conditions as in simulations. From the comparison, we can see that the proposed model predictive control scheme reduces the grade change time and achieves stable steady-state.

• Journal of Preventive Medicine and Public Health
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• 제16권1호
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• pp.31-34
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• 1983

For the study of the applicability of predictive equation of subjective symptoms of workers using organic solvents, 67 male workers using organic solvents were selected. The predictive equation was quotated from previous Cheon's study. All the datas of necessary variables were gained. The predictive values and observed values were compared. The results were summarized as below; 1. The difference between predictive values and observed values were characteristically large. The coefficient of determination between the two values was 0.0024. 2. The $R^2$ value of multiple stepwise regression equation derived from present study was 0.2827.

• Journal of Electrical Engineering and Technology
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• 제6권3호
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• pp.423-430
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• 2011

Multi-input multi-output (MIMO) dynamic matrix control (DMC) technique is applied to control steam temperatures in a large-scale ultrasupercritical once-through boiler-turbine system. Specifically, four output variables (i.e., outlet temperatures of platen superheater, finish superheater, primary reheater, and finish reheater) are controlled using four input variables (i.e., two spray valves, bypass valve, and damper). The step-response matrix for the MIMO DMC is constructed using the four input and the four output variables. Online optimization is performed for the MIMO DMC using the model predictive control technique. The MIMO DMC controller is implemented in a full-scope power plant simulator with satisfactory performance.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.227-242
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• 2015

This paper proposes a weighting factor optimization method in predictive control algorithm for torque ripple reduction in an induction motor fed by an indirect matrix converter (IMC). In this paper, the torque ripple behavior is analyzed to validate the proposed weighting factor optimization method in the predictive control platform and shows the effectiveness of the system. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponds to minimum torque ripple and is compared with the results of conventional weighting factor based predictive control algorithm. The predictive control algorithm selects the optimum switching state that minimizes a cost function based on optimized weighting factor to actuate the indirect matrix converter. The conventional and introduced weighting factor optimization method in predictive control algorithm are validated through simulations and experimental validation in DS1104 R&D controller platform and show the potential control, tracking of variables with their respective references and consequently reduces the torque ripple.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.697-699
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• 2000

In this paper, an extension of model predictive controller for nonlinear process using Takagi-Sugeno(TS) fuzzy model is proposed Since the consequent parts of TS fuzzy model comprise linear equations of input and output variables. it is locally linear, and the Generalized Predictive Control(GPC) technique which has been developed to control Linear Time Invariant(LTI) plants, can be extended as a parallel distributed controller. Also fuzzy soft constraints are introduced to handle both equality and inequality constraints in a unified form. So the traditional constrained GPC can be transferred to a standard fuzzy optimization problem. The proposed method conciliates the advantages of the fuzzy modeling with the advantages of the constrained predictive control, and the degree of freedom is increased in specifying the desired process behavior.

• 펄프종이기술
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• 제37권1호
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• pp.61-66
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• 2005

The grade change operations In paper mills exhibit inherent nonlinear dynamic characteristics. For this reason, the conventional model predictive control techniques based on linear process models are not adequate for the grade change operations. In this paper, a bilinear model for the nonlinear grade change processes was presented first and optimal input variables were calculated by using one-step-ahead predictive control method. Numerical simulations showed that the control performance lied within acceptable range and that the bilinear model predictive control scheme was highly promising control strategy for the grade change operations.

• 한국병원경영학회지
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• 제4권2호
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• pp.354-368
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• 1999

This research aims to compare between regression and neural network in terms of the predictive ability of the overhead costs in hospitals. For this purpose, this research uses the number of out-patients and complex medical treatments as explaining variables. Thirty-one hospitals were used for the empirical test The test result shows that the regression model has a more predictive ability than the neural network.

• 제어로봇시스템학회논문지
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• 제21권7호
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• pp.662-668
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• 2015

This paper presents an improved predictive control of an induction machine fed by a matrix converter using N-switching vectors as the control action during a complete sampling period of the controller. The conventional model predictive control scheme based matrix converter uses a single switching vector over the same period which introduces high torque ripple. The proposed switching scheme for a matrix converter based model predictive control of an induction machine drive selects the appropriate switching vectors for control of electromagnetic torque with small variations of the stator flux. The proposed method can reduce the ripple of the electrical variables by selecting the switching state as well as the method used in the space vector modulation techniques. Simulation results are presented to verify the effectiveness of the improved predictive control strategy for induction machine fed by a matrix converter.