• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.99-100
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• 2016

In a AC motor used by three phase inverter, the phase current must be measured to control instantaneous torque. It is expensive to use current sensor for measuring current in low cost motor. So, shunt resistor is used to measure current. But, the method sensing the phase current using shunt resistor cannot perform the vector control in high speed because of the area that impossible to restore three phase current. In this paper, predictive current is proposed for reconstructing the current in the impossible current sensing area that reduce the current ripple in TSSI(Three shunt sensing inverter) for PMSM.

• Proceedings of the KIPE Conference
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• 2001.12a
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• pp.198-203
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• 2001

In this paper, a new predictive control algorithm considering the parameters of series active filter has been proposed to improve the performance. By using the proposed control scheme, the current ripple drastically reduced and an improved steady state performance can be obtained. The proposed method has another advantage in the size. and cost by excluding additional passive fitters. The validity of the proposed method will be proved by the computer simulation.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1991.10a
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• pp.53-58
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• 1991

This paper presents the speed control of DC motor based on deadbeat response method. Since 송 deadbeat response systems are characterized by the discrete time control, the instabiliby of the systems caused by saturation and time lag problems is inevitable. In order to release these problems, we propose a compensator utilizing the predictive control so that the fast response can be also achieved in the saturation state. Experimental results demonstrated that outputs are able to settle final values in on sampling time for unsaturated reference inputs. For saturated reference inputs, outputs take one sampling time after getting free from the saturation state. Further we are able to settle the fast response with suppressed overshoot by appling the predictive control.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.601-606
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• 1993

From the power plants in a steel plant, environment pollutants such as SOx, NOx, CO are emitted by the combustion reaction between the fuels those are by-product gases and oil. To reduce the amounts of the pollutants, it must be important that build the predictive models for the emission of the pollutants. In this paper, the model that predict the amount of future fuel consumption and the model that predict the amount of generated pollutants for the used fuel amounts is developed by using Gibb's free energy minimization method with the temperature correction techniques and neural network back propagation method. For some data set, the calculation results from this models are compared with the real emission amounts of SOx, NOx and result of the calculation by the ASPEN plus which is a commercial software. The result from this model is better than the result by ASPEN plus for this problem.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.159-163
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• 2013

Background: This study used Surveillance, Epidemiology and End Results (SEER) pancreatic cancer data to identify predictive models and potential socio-economic disparities in pancreatic cancer outcome. Materials and Methods: For risk modeling, Kaplan Meier method was used for cause specific survival analysis. The Kolmogorov-Smirnov's test was used to compare survival curves. The Cox proportional hazard method was applied for multivariate analysis. The area under the ROC curve was computed for predictors of absolute risk of death, optimized to improve efficiency. Results: This study included 58,747 patients. The mean follow up time (S.D.) was 7.6 (10.6) months. SEER stage and grade were strongly predictive univariates. Sex, race, and three socio-economic factors (county level family income, rural-urban residence status, and county level education attainment) were independent multivariate predictors. Racial and socio-economic factors were associated with about 2% difference in absolute cause specific survival. Conclusions: This study s found significant effects of socio-economic factors on pancreas cancer outcome. These data may generate hypotheses for trials to eliminate these outcome disparities.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.27.1-27
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• 2001

Reheating furnace is a facility of heating up the billet to desired high temperature in the hot charge rolling process and it consists of 3 zones. Temperature control of reheating furnace is essential for successful rolling performance and high productivity. Mostly, temperature control is carried out using PID controller However, the PID control is not effective due to the nonlinearity of the reheating furnace(i.e, presence of the interference of neighboring zones and slow response of temperature etc.). In this paper, feedback linearization method is applied to obtain a linear model of the reheating furnace. Then, controller is designed using simple predictive control method. The effectiveness of this strategy is shown through simulations.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.130-139
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• 1994

Video compression algorithms are based on removing spatial and/or temproal redundancy inherent in image sequences by predictive(DPCM) encoding, transform encoding, or a combination of predictive and transform encoding. In this paper, each 8$\times$8 DCT coefficient of DFD(displaced frame difference) is adaptively quantized by one of the four quantizers depending on total distortion level, which is determined by characteristics of HVS(human visual system) and buffer status. Therefore, the number of possible quantizer selection vectors(patterns) is 4$^{64}$. If this vectors are coded, toomany bits are required. Thus, the quantizer selection vectors are limited to 2048 for Y and 512 for each U, V by the proposed method using SWAD(sum of weighted absolute difference) for discriminating vectors. The computer simulation results, using the codebook vectors which are made by the proposed method, show that the subjective and objective image quality (PSNR) are goor with the limited bit allocation. (17Mbps)

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.1
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• pp.1-8
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• 1982

Many researches on the parametric representation of speech ,signals using the adaptive linear prediction method have been studied for the past few years. In this paper, we used the LPC(Linear Predictive Coding)method to analyae the parameters of Korean vowels and by using those parameters we studied the affinity between every pair of Korean vowels. As a result of our study, it is found that each pair of Korean vowels that has a greater phonetic affinity also has a greater affinity of vocal tract parameters than other pairs.

• Research in Community and Public Health Nursing
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• v.17 no.2
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• pp.253-262
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• 2006

Purpose: This study was done to evaluate the predictive factors of smoking cessation intention of smoking adolescents at H. district in Chungchungnam-do. Method: A convenience sample was recruited from a public health center at H. district in Chungchungnam-do. A total of 100 smoking adolescents were enrolled in this study. A self-report survey method was used to identify the predictive factors related to smoking cessation. Result: A forward stepwise logistic regression analysis identified four factors associated with smoking cessation intention of smoking adolescents: accompanied friends during the smoking cessation program (OR=20.14), preparation for smoking cessation (OR=5.12), smoking cessation knowledge after the smoking cessation program (OR=1.41), and the number of cigarettes (OR=0.15). Conclusion: Based on this study results, the effective programs in reducing adolescent smoking rates should include components to accompany peers, increase the knowledge of smoking impact, and the benefit of smoking cessation.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1419-1425
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• 2000

When we predict of software reliability, we can't know the testing stopping time and how many faults be residues in software the (the maximum value of data) during these software testing process, therefore we assume the maximum value and the training result can be inaccuracy. In this paper, we present neural network approach for software reliability prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data.