• Journal of Electrical Engineering and Technology
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• v.10 no.1
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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.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.231-238
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• 2023

For boost PFC (Power Factor Correction) converters, various control methods are being studied to achieve unity power factor and low THD (Total Harmonic Distortion) of AC input current. Among them, average current mode control, which controls the average value of the inductor current to follow the current reference, is the most widely used. However, nowadays, as advanced digital control becomes possible with the development of digital processors, predictive control of boost PFC converters is receiving attention. Predictive control is classified into predictive current mode control, which generates duty in advance using a predictive algorithm, and model predictive current control, which performs switching operations by selecting a cost function based on a model. Therefore, this paper simply explains the average current mode control, predictive current mode control, and model predictive current control of the boost PFC converter. In addition, current control under entire load and disturbance conditions is compared and analyzed through simulation.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.477-491
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• 1997

A new predictive classification rule for assigning future cases into one of two multivariate normal population (with unknown normal mixture model) is considered. The development involves calculation of posterior probability of each possible normal-mixture model via a default Bayesian test criterion, called intrinsic Bayes factor, and suggests predictive distribution for future cases to be classified that accounts for model uncertainty by weighting the effect of each model by its posterior probabiliy. In this paper, our interest is focused on constructing the classification rule that takes care of uncertainty about the types of covariance matrices (homogeneity/heterogeneity) involved in the model. For the constructed rule, a Monte Carlo simulation study demonstrates routine application and notes benefits over traditional predictive calssification rule by Geisser (1982).

• Nuclear Engineering and Technology
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• v.33 no.1
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• pp.102-110
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• 2001

In this work, the model predictive control method was applied to a linear model and a nonlinear model of steam generators. The parameters of a linear model for steam generators are very different according to the power levels. The model predictive controller was designed for the linear steam generator model at a fixed power level. The proposed controller at the fixed power level showed good performance for any other power levels by designed changing only the input-weighting factor. As the input-weighting factor usually increases, its relative stability does so. The steam generator has some nonlinear characteristics. Therefore, the proposed algorithm has been implemented for a nonlinear model of the nuclear steam generator to verify its real performance and also, showed good performance.

• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.209-210
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• 2015

This paper presents a new model predictive control method without the effect of a weighting factor in order to reduce common-mode voltage (CMV) for a three-phase voltage source inverter (VSI). By utilizing two active states with same dwell time during a sampling period instead of one state used in conventional method, the proposed method can reduce the CMV of VSI without the weighting factor. Simulation is carried out to verify the effectiveness of the proposed predictive control method with the aid of PSIM software.

• Journal of Power Electronics
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• v.15 no.5
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• pp.1168-1177
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• 2015

This paper presents a scheme to improve the line current distortion of power factor corrector (PFC) topology at the zero crossing point using a predictive control algorithm in both the continuous conduction mode (CCM) and discontinuous conduction mode (DCM). The line current in single-phase PFC topology is distorted at the zero crossing point of the input AC voltage because of the characteristic of the general proportional integral (PI) current controller. This distortion degrades the line current quality, such as the total harmonic distortion (THD) and the power factor (PF). Given the optimal duty cycle calculated by estimating the next state current in both the CCM and DCM, the proposed predictive control algorithm has a fast dynamic response and accuracy unlike the conventional PI current control method. These advantages of the proposed algorithm lower the line current distortion of PFC topology. The proposed method is verified through PSIM simulations and experimental results with 1.5 kW bridgeless PFC (BLPFC) topology.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1398-1408
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• 2018

In order to further improve the steady-state control performance of model predictive torque control (MPTC), a double-vector-based model predictive torque control without a weighting factor is proposed in this paper. The extended voltage vectors synthesized by two basic voltage vectors are used to increase the number of feasible voltage vectors. Therefore, the control precision of the torque and the stator flux along with the steady-state performance can be improved. To avoid testing all of the feasible voltage vectors, the solution of deadbeat torque control is calculated to predict the reference voltage vector. Thus, the candidate voltage vectors, which need to be evaluated by a cost function, can be reduced based on the sector position of the predicted reference voltage vector. Furthermore, a cost function, which only includes a reference voltage tracking error, is designed to eliminate the weighting factor. Moreover, two voltage vectors are applied during one control period, and their durations are calculated based on the principle of reference voltage tracking error minimization. Finally, the proposed method is tested by simulations and experiments.

• Journal of Preventive Medicine and Public Health
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• v.34 no.3
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• pp.237-243
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• 2001

Objectives : To examine the screening rate of cervical cancer in women and to find out the predictive factors for participation in cervical cancer screening programs within their life-time and within the last two years. Methods : The data was based on self-reported questionnaires from 1,613 women whose ages ranged from 26 to 60 years; this survey was peformed between December 1999 and January 2000. This study analyzed the predictive factors for participation in cervical cancer screening programs within their life-time and within the last two years. A logistic regression analysis was performed in order to derive the significant variables from the predisposing factors(demographic factor, health promotion behavior, reproductive factor), intervention factors(information channel, relation with medical stan, and proximal factors(attitude, social influence, self-efficacy). All analyses were peformed by the PC-SAS 6.12. Results : Our analyses showed that the screening rate for the women who received a cervical cancer screening(Pap smear) more than once within their life-time was 56.1% while those who had received one within the last two years was 34.5%. The significant factors for participation in cervical cancer screening program within their life-time were their income, married age, health promotion score, relation with medical staffs, social influence, and self-efficacy. On the other hand, age, number of pregnancies, menarche age, relation with medical staffs, social influences, and self-efficacy were significant factors for those being screened within the last two years. The predictive power of the logit model within their life-time was 68.8% and that within the last two years was 66.6%. Conclusion : The predictive factors for participation in cervical cancer screening program within their life-time are different from those for within the last two years. and that women's relations with medical staffs and social influences were the critical factors impacting on cervical cancer screening rates.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.11
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• pp.1-10
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• 2021

This paper compares the performance of Fama-French three-factor and five-factor models using a dataset of 20 Pakistani commercial banks for the period 2011 to 2020. We focus on an emerging economy as the findings from earlier studies on developed countries cannot be generalized in emerging markets. For empirical analysis, twelve portfolios were developed based on size, market capitalization, investment strategy, and growth. Subsequently, we constructed five Fama-French factors namely, R_M, SMB, HML, RMW, and CMA. The OLS regression technique with robust standard errors was applied to compare the predictive power of both the Fama-French models. Further, we also compared the mean-variance efficiency of the Fama-French models through the GRS test. Our empirical analysis provides three unique and interesting findings. First, both asset pricing models have similar predictive power to explain the expected portfolio returns in most cases. Second, our results from the GRS test suggest that there is no noticeable difference in the mean-variance efficiency of one asset pricing model over the other. Third, we find that all factors of both Fama-French models are statistically significant and are important for explaining the volatility of expected commercial bank returns in the context of Pakistan.

• The Korean journal of helicobacter and upper gastrointestinal research
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• v.18 no.3
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• pp.186-197
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• 2018

Background/Aims: To investigate the predictive factors for improvement of atrophic gastritis (AG) and intestinal metaplasia (IM). Materials and Methods: A total of 778 subjects were prospectively enrolled and followed up for 10 years. Histological analysis of AG and IM was performed by using the updated Sydney system. To find the predictive factors for reversibility of AG and IM, 24 factors including genetic polymorphisms and bacterial and environmental factors were analyzed. Results: In all subjects, the predictive factor by multivariate analysis for improvement of both antral and corpus AG was successful eradication. The predictive factors for improvement of antral IM were age and successful eradication. The predictive factor for improvement of corpus IM was successful eradication. In patients with Helicobacter pylori infection, age and cagA were predictive factors for improvement of AG and IM. In patients with H. pylori eradication, monthly income and cagA were predictive factors for improvement of AG and IM. Conclusions: H. pylori eradication is an important predictive factor of regression of AG and IM and would be beneficial for the prevention of intestinal-type gastric cancer. Young age, high income, and cagA are additional predictive factors for improving AG and IM status. Thus, various factors affect the improvement of AG and IM.