• 전기학회논문지P
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• 제51권4호
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• pp.206-210
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• 2002

The transmission delays in the networked control systems affect the performance and stability. For covering the delays, this paper proposes NCS including the predictive strategy. The proposed method shows that the two cases such as stable and unstable system are well behaved.

• 종양간호연구
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• 제3권2호
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• pp.122-132
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• 2003

Purpose: The purpose of this study was to identify predictive factors of Brest Self-Examination practice of clinical nurses. Method: The subject for this study were 277 nurses in 8 university hospitals in Busan. The data were collected from September 21 to October 20, 2001 by means of a structure questionnaire. The instruments used for this study were Choi's BSE knowledge scale. Kim's BSE attitude scale and Jung's BSE practice scale. The data were analyzed using frequency, percentage, mean, Peason Correlation, t-teat, ANOVA, scheffe's test, and multiple stepwise Regression using SPSS program. Result: 1. The mean score of BSE practice for the total sample was 7. 25${\pm}$4.62. 2. Statistically significant factors influencing the BSE Practice among social demographic characteristics were age(F=2.734, P=0.44), Married status(t=2.598, p=0.010). 3. Statistically significant factors influencing the BSE Practice among BSE relating characteristics were enlisting the help of significant peers(t=3.34, P=0.00), Intention of Practice for BSE(t=10.462, p=0.00), performance of BSE(t=7.800, P=0.00), frequency of performance in BSE(F=13.932, p=0.00), confidence in Knowledge of BSE technique(F=5.350, p=0.00), confidence in finding breast nodule(F=7.204, p=.00), asking client's BSE (t=3.153, P=0.01). 4.The mild correlation between nurse's BSE knowledge and practice was found(r=0.366,p=0.000). 5. There were significant predictors of BSE Practice. Performance of BSE was the best significant predictive factor(R2=.383, p=.000) Another significant predictive factors were knowledge, intension of practice, married status, frequency of performance. Conclusion: Degree of nurses' performance of BSE was average. It is necessary to develope the nurses' educational program for BSE with its focus on above predictive factors of performance of BSE.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제26권1호
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• pp.23-48
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• 2022

In this paper, we provide predictive models for the market price of fruits, and analyze the performance of each fruit price predictive model. The data used to create the predictive models are fruit price data, weather data, and Korea composite stock price index (KOSPI) data. We collect these data through Open-API for 10 years period from year 2011 to year 2020. Six types of fruit price predictive models are constructed using the LSTM algorithm, a special form of deep learning RNN algorithm, and the performance is measured using the root mean square error. For each model, the data from year 2011 to year 2018 are trained to predict the fruit price in year 2019, and the data from year 2011 to year 2019 are trained to predict the fruit price in year 2020. By comparing the fruit price predictive models of year 2019 and those models of year 2020, the model with excellent efficiency is identified and the best model to provide the service is selected. The model we made will be available in other countries and regions as well.

• Advances in Computational Design
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• 제7권2호
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• 설비공학논문집
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• 제13권3호
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• pp.200-208
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• 2001

In indoor temperature control of a heat pump, a reduction in energy consumption is very important. However, most control schemes for heat pumps have focused only on control performance such s settling time and steady-state error. In this paper, the model predictive control (MPC) which includes the energy-related variable in this cost function is proposed. By computing the control signal minimizing this cost function, the trade-off between energy reduction and temperature control performance can be obtained. Since the MPC required the process model, the dynamic mode of a heat pump is also obtained by the system identification technique. Performance of the proposed MPC considering energy efficiency is compared with the two other control schemes. It si shown that the proposed scheme can consume less energy thant hte others in achieving similar control performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.608-613
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• 2004

This paper is concerned with the design of a predictive PID controller, which has similar features to the model-based predictive controller. A PID type control structure is defined which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are pre-calculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with generalized predictive controller and the results are compared with generalized predictive control solutions.

• 제어로봇시스템학회논문지
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• 제2권4호
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• pp.344-350
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• 1996

An adaptive predictive level control of waste heat steam boiler was studied by using mathematical models considering the inverse response. The simulation experiments of the model identification were performed by using linear and bilinear models. From the results of simulations it was found that the bilinear model represented the actual dynamic behavior of steam boiler very well. ARMA model was used in the model identification and the adaptive predictive controller. To verify the performance and effectiveness of the adaptive predictive controller used in this study the simulation results of the adaptive predictive level control for waste heat steam boiler based on bilinear model were compared to those of P, PI and PID controller. The results of simulations showed that the adaptive predictive controller provides the fast arrival to setpoint of liquid level.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권1호
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• pp.69-75
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• 2005

This paper is concerned with the design of a predictive PID controller, which has similar features to the model-based predictive controller. A PID type control structure is defined which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are pre-calculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with generalized predictive controller and the results are compared with generalized predictive control solutions.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.285-289
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• 1996

Fuzzy logic controllers have been shown better performance than conventional ones especially in highly nonlinear plants. These results are caused by the nonlinear fuzzy rules were not sufficient to cope with significant uncertainty of the plants and environment. Moreover, it is hard to make fuzzy rules consistent and complete. In this paper, we employed a predictive neural network to enhance the nonlinear inference capability. The predictive neural network generates predictive outputs of a controlled plant using the current and past outputs and current inputs. These predictive outputs are used in terms of fuzzy rules in fuzzy inferencing. From experiments, we found that the predictive term of fuzzy rules enhanced the inference capability of the controller. This predictive neural network can also help the controller cope with uncertainty of plants or environment by on-line learning.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2002년도 춘계학술대회논문집
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• pp.219-227
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• 2002

In this paper, we designed neural network predictive PID controller to control sway happened in transfer of trolley for automatic travel control system. We include dynamic character of nonlinear system, and mathematical expression veny simple used neural network. When various establishment location and surrounding disturbance were approved based on mathematical modelling of crane, controller designed to become effective control location error and vibration angle of two control variables that simultaneously can predictive control. Neural network predictive PID controller produced parameter of PID controller using neural network self-tuner. Neural network self-tuner's input used crane's output and neural network predictive output. Neural network self-tuner using error back propagation algorithm. We analyzed control performance comparison through computer simulation when applied disturbance about sway of location and angle in transfer of crane. The results show that the proposed neural network predictive PID controller has better performances than general PID controller, neural network PID controller.