• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.145-145
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• 2000

In this paper, we propose a hybrid d-step predictor which is composed of an adaptive predictor and a Kalman predictor. We prove the performance limit of the proposed predictor. Simulation is conducted to examine the performance of the proposed predictor. Simulation results show that the proposed combined predictor is superior to the adaptive predictor and the Kalman predictor. Proposed predictor is used for prediction of gun tip vibration of k1 tank. The result is compared with that of conventional adaptive predictor.

• Journal of the Korean Geosynthetics Society
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• v.17 no.2
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• pp.9-18
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• 2018

In this study, a field model experiment was performed to analyze the bahavior of slope during failure. It was analyzed through x-MR control chart method with inverse displacement and K-value. As a result, the portent was confirmed at 4 minutes before slope failure in Case 1. The change of the control limit line according to moving range was analyzed and it was effective to apply K = 3. Use of the inverse displacement and x-MR control chart method will be useful for the prediction of abnormal behavior through quick and objective judgment. Prediction of slope failure using control chart method can be used as basic data of slope measurement management standard, and it can contribute in reduction of life and property damage caused by slope disaster.

• Journal of Korean Society of Forest Science
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• v.96 no.3
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• pp.300-306
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• 2007

This study was carried out to analyze the characteristics of forest environmental and stream morphological factors by using the quantification theory (I) for prediction of the suitable dam site. The results obtained from this study were summarized as follows; The selection of suitable site for erosion control dam was estimated by normalized score of each category. And the prediction method of suitable site for erosion control dam divided into class I (Very suitable site), II (Suitable site), and III (Poor suitable site) for the convenience of use. In conclusion, if we select the suitable site for construction of erosion control dam for disaster prevention, we could save the loss of tremendous budget, avoid the poor suitable site due to subjective judgment, and it would promote the functions of erosion control dam.

• Research in Community and Public Health Nursing
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• v.13 no.3
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• pp.456-470
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• 2002

Objectives: The purpose of this study was to test the Theory of Planned Behavior (TPB) in the prediction of smoking cessation intention and smoking cessation behavior among adolescent smokers, in order to provide basic data to develop a future smoking cessation program as a nursing intervention. Method: The study subjects were 80 adolescent smokers who had smoked one cigarette and attended a five-day school smoking cessation program. The data were collected from October 24 to December 21, 1999. The instruments used in this study were the tools developed by Jee (1994) to measure TPB variables such as attitude toward smoking cessation behavior, subjective norm, perceived behavioral control, smoking cessation intention, and smoking cessation behavior. The data were analyzed with the SAS/PC program using descriptive statistics, hierarchical multiple regression, and logistic multiple regression. Results: 1. Attitude toward smoking cessation behavior, subjective norm, and perceived behavioral control were partially significant in predicting smoking cessation intention. 2. Smoking cessation intention and perceived behavioral control toward smoking cessation behavior did not significantly predict smoking cessation behavior. 3. There were partial interaction effects among the attitude toward smoking cessation behavior, subjective norm, and perceived behavioral control in the prediction of smoking cessation intention. 4. There were partial interaction effects between smoking cessation intention and perceiver behavioral control toward smoking cessation behavior in the prediction of smoking cessation behavior. Conclusion: This study partially demonstrated support for the TPB model that was partially useful in predicting smoking cessation intention and smoking cessation behavior among adolescent smokers. Therefore, it is recommended that attitude toward smoking cessation behavior and perceived behavioral control should be considered in developing smoking cessation programs and implementing nursing interventions to change the smoking behavior of adolescent smokers.

• KIEAE Journal
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• v.17 no.5
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• pp.69-76
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• 2017

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.440-445
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• 2016

There is a scheme where secondary users (SU) use predicted spectrum holes for primary users (PU) not to utilize for efficient utilization of the limited spectrum resources in cognitive radio networks. In this paper, we propose an adaptive call admission control framework that minimizes spectrum hopping call dropped probability (SHDP) for satisfying SU quality of service (QoS). The scheme is based on a call admission control (CAC), bandwidth prediction and adaptive bandwidth assignment. The prediction model predicts not only the number of spectrum holes, but requested bandwidth of SU spectrum hopping call, and then the CAC minimizes SHDP via an adaptive bandwidth assignment in resources not being enough for reservation. We bring Wiener prediction model to predict the resources. Simulations are conducted to compare the performance of proposed scheme with an existing, and show its ability of minimizing the SHDP.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.22-28
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• 2014

This paper proposes the fuzzy modeling and robust stability analysis of wind farm based on prediction model for wind speed. Owing to the sensitivity of wind speed, it is necessary to study the dynamic equation of the variable speed wind turbine. In this paper, based on the least-square method, the wind speed prediction model which is varied by the surrounding environment is proposed so that it is possible to evaluate the practicability of our model. And, we propose the composition of intelligent wind farm and use the fuzzy model which is suitable for the design of fuzzy controller. Finally, simulation results for wind farm which is modeled mathematically are demonstrated to visualize the feasibility of the proposed method.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.1
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• pp.24-32
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• 2006

In this paper, we proposed the multimedia call admission control algorithm with the bandwidth reservation based on the prediction of wireless terminal's location to guarantee quality of service for multimedia applications in cellular networks. This algorithm aims at minimizing possible errors In predicting the moving direction of terminals using a mobility prediction scheme. This prediction reduces the size of bandwidth reserved redundantly. In order to evaluate the performance of the algorithm, the blocking rate of new calls and the forced termination rate of hand-off calls are measured and compared the results with those of existing schemes. The results of the experiment revealed that the algorithm presented in this paper achieved better performance with lower call blocking rates and forced-termination rates than those of other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.412-417
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• 2005

The Internet has been widely applied to the remote control system. The network-based control system, however, has a random time delay and an inherent weak point of the network, when the data ate transmitted. The network delay may result in performance degradation or even system instability in teleoperation. In this paper a prediction model of network delay using TSK (Takagi-Sugeno-Kang) fuzzy model is presented. An adaptive scheme is developed to update the prediction model according to the current network status. The prediction model is applied to the control of an Internet-based mobile robot to show its usefulness. In the computer simulation the TSK Prediction model of network delay is proven superior to the conventional algorithms.

• Journal of Korean Society on Water Environment
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• v.29 no.2
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• pp.212-222
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• 2013

A watershed model was constructed using Hydrological Simulation Program Fortran to quantitatively predict the stream flows at major tributaries of Nakdong River basin, Korea. The entire basin was divided into 32 segments to effectively account for spatial variations in meteorological data and land segment parameter values of each tributary. The model was calibrated at ten tributaries including main stream of the river for a three-year period (2008 to 2010). The deviation values (Dv) of runoff volumes for operational stream flow forecasting for a six month period (2012.1.2 to 2012.6.29) at the ten tributaries ranged from -38.1 to 23.6%, which is on average 7.8% higher than those of runoff volumes for model calibration (-12.5 to 8.2%). The increased prediction errors were mainly from the uncertainties of numerical weather prediction modeling; nevertheless the stream flow forecasting results presented in this study were in a good agreement with the measured data.