• Journal of Power Electronics
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• v.18 no.6
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• pp.1889-1900
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• 2018

Harmonic current mitigation is vital in power distribution networks owing to the inflow of nonlinear loads, distributed generation, and renewable energy sources. The active power filter (APF) is the current electrical equipment that can dynamically compensate for harmonic distortion and eliminate asymmetrical loads. The compensation performance of an APF largely depends on the control strategy applied to the voltage source inverter (VSI). Model predictive control (MPC) has been demonstrated to be one of the effective control approaches to providing fast dynamic responses. This approach covers different types of power converters due to its several advantages, such as flexible control scheme and simple inclusion of nonlinearities and constraints within the controller design. In this study, a finite control set-MPC technique is proposed for the control of VSIs. Unlike conventional control methods, the proposed technique uses a discrete time model of the shunt APF to predict the future behavior of harmonic currents and determine the cost function so as to optimize current errors through the selection of appropriate switching states. The viability of this strategy in terms of harmonic mitigation is verified in MATLAB/Simulink. Experimental results show that MPC performs well in terms of reduced total harmonic distortion and is effective in APFs.

• 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.

• Food Science of Animal Resources
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• v.37 no.4
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• pp.579-592
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• 2017

This study assessed the quantitative microbial risk of non-enterohemorrhagic Escherichia coli (EHEC). For hazard identification, hazards of non-EHEC E. coli in natural and processed cheeses were identified by research papers. Regarding exposure assessment, non-EHEC E. coli cell counts in cheese were enumerated, and the developed predictive models were used to describe the fates of non-EHEC E. coli strains in cheese during distribution and storage. In addition, data on the amounts and frequency of cheese consumption were collected from the research report of the Ministry of Food and Drug Safety. For hazard characterization, a doseresponse model for non-EHEC E. coli was used. Using the collected data, simulation models were constructed, using software @RISK to calculate the risk of illness per person per day. Non-EHEC E. coli cells in natural- (n=90) and processed-cheese samples (n=308) from factories and markets were not detected. Thus, we estimated the initial levels of contamination by Uniform distribution ${\times}$ Beta distribution, and the levels were -2.35 and -2.73 Log CFU/g for natural and processed cheese, respectively. The proposed predictive models described properly the fates of non-EHEC E. coli during distribution and storage of cheese. For hazard characterization, we used the Beta-Poisson model (${\alpha}=2.21{\times}10^{-1}$, $N_{50}=6.85{\times}10^7$). The results of risk characterization for non-EHEC E. coli in natural and processed cheese were $1.36{\times}10^{-7}$ and $2.12{\times}10^{-10}$ (the mean probability of illness per person per day), respectively. These results indicate that the risk of non-EHEC E. coli foodborne illness can be considered low in present conditions.

• The Korean Journal of Pain
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• v.34 no.3
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• pp.339-345
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• 2021

Background: Ultrasound-guided caudal epidural injection (CEI) is limited in that it cannot confirm drug distribution at the target site without fluoroscopy. We hypothesized that visualization of solution flow through the inter-laminar space of the lumbosacral spine using color Doppler ultrasound alone would allow for confirmation of drug distribution. Therefore, we aimed to prospectively evaluate the usefulness of this method by comparing the color Doppler image in the paramedian sagittal oblique view of the lumbosacral spine (LS-PSOV) with the distribution of the contrast medium observed during fluoroscopy. Methods: Sixty-five patients received a 10-mL CEI of solution containing contrast medium under ultrasound guidance. During injection, flow was observed in the LSPSOV using color Doppler ultrasonography, following which it was confirmed using fluoroscopy. The presence of contrast image at L5-S1 on fluoroscopy was defined as "successful CEI." We then calculated prediction accuracy for successful CEI using color Doppler ultrasonography in the LS-PSOV. We also investigated the correlation between the distribution levels measured via color Doppler and fluoroscopy. Results: Prediction accuracy with color Doppler ultrasonography was 96.9%. The sensitivity, specificity, positive predictive value, and negative predictive value were 96.7%, 100%, 100%, and 60.0%, respectively. In 52 of 65 patients (80%), the highest level at which contrast image was observed was the same for both color Doppler ultrasonography and fluoroscopy. Conclusions: Our findings demonstrate that color Doppler ultrasonography in the LS-PSOV is a new method for determining whether a drug solution reaches the lumbosacral region (i.e., the main target level) without the need for fluoroscopy.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.71-77
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• 1997

S independent sample 0,1,2, $\cdots$, s-1 (or stages 0,1,2, $\cdots$, s-1) are available from the Raleigh population. Procedure for predicting any order statistic in the $(s+1)^{th}$ sample is developed by obtaining the predictive distribution at stage s. Bounds for the sample size at stage S, in order to have the variance at stage S less than that at stage (s-1), are obtained.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.948-951
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• 2000

This paper presents the analytical and experimental methodology for the prediction of aerosol concentration and size distribution due to cutting fluid atomization mechanism in turnining operation. The established analytical model which is based on atomization theory analyzes the cutting fluid motion and aerosol generation in machining process. The impinging and evaporation experiments were performed to know the particle size and evaporation rate of cutting fluid. The predictive models can be used as a basis for environmental impact analysis on the shop floor. It can be also facilitate the optimization of cutting fluid usage in achieving a balanced consideration of productivity and environmental consciousness.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.177-190
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• 2006

The aim of this study is to propose a Bayesian model for fitting mortality rate of colon cancer. For the analysis of mortality rate of a disease, factors such as age classes of population and spatial characteristics of the location are very important. The model proposed in this study allows the age class to be a random effect in addition to its conventional role as the covariate of a linear regression, while the spatial factor being a random effect. The model is fitted using Metropolis-Hastings algorithm. Posterior expected predictive deviances, standardized residuals, and residual plots are used for comparison of models. It is found that the proposed model has smaller residuals and better predictive accuracy. Lastly, we described patterns in disease maps for colon cancer.

• Fire Science and Engineering
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• v.21 no.2 s.66
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• pp.14-23
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• 2007

In this thesis, we analysed urban fires and developed the predictive mapping technique by using GIS and spatial statistics. It presented the correlation between the fire data of last 5 years ($2001{\sim}2005$) and the factor of civilization environment in Daegu city. We produced a model of fire hazard predictive map by analyzing uncertainty of fire with the quadrat analysis and the poisson distribution.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1076-1080
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• 2015

An inventory control system was developed for a distribution system consisting of a single multiproduct warehouse serving a set of customers and purchasing products from multiple vendors. Purchase orders requesting multiple products are delivered to the warehouse in a process. The receipt of customer orders by the warehouse proceeded in order intervals and in order quantities that are subject to random fluctuations. The objective of warehouse operation is to minimize the total cost while maintaining inventory levels within the warehouse capacity by adjusting the purchase order intervals and quantities. An adaptive model predictive control algorithm was developed using a periodic square wave model to represent the material flows. The adaptive concept incorporated a stabilized minimum variance control-type input calculation coupled with input/output stream parameter predictions. The effectiveness of the scheme was demonstrated using simulations.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.